Immuno-inspired robotic applications: a review
---
paper_title: Application areas of AIS: The past, the present and the future

paper_content:

After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories-however, if the field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the past, and ask the difficult question ''was it worth it ?''. We then attempt to suggest a set of problem features that we believe will allow the true potential of the immunological system to be exploited in computational systems, and define a unique niche for AIS.
---
paper_title: Applying inter-layer conflict resolution to hybrid robot control architectures

paper_content:

In this document, we propose and examine the novel use of a learning mechanism between the reactive and deliberative layers of a hybrid robot control architecture. Balancing the need to achieve complex goals and meet real-time constraints, many modern mobile robot navigation control systems make use of a hybrid deliberative-reactive architecture. In this paradigm, a high-level deliberative layer plans routes or actions toward a known goal, based on accumulated world knowledge. A low-level reactive layer selects motor commands based on current sensor data and the deliberative layer’s plan. The desired system-level effect of this architecture is that the robot is able to combine complex reasoning toward global objectives with quick reaction to local constraints. ::: Implicit in this type of architecture, is the assumption that both layers are using the same model of the robot’s capabilities and constraints. It may happen, for example, due to differences in representation of the robot’s kinematic constraints, that the deliberative layer creates a plan that the reactive layer cannot follow. This sort of conflict may cause a degradation in system-level performance, if not complete navigational deadlock. Traditionally, it has been the task of the robot designer to ensure that the layers operate in a compatible manner. However, this is a complex, empirical task. ::: Working to improve system-level performance and navigational robustness, we propose introducing a learning mechanism between the reactive layer and the deliberative layer, allowing the deliberative layer to learn a model of the reactive layer’s execution of its plans. First, we focus on detecting this inter-layer conflict, and acting based on a corrected model. This is demonstrated on a physical robotic platform in an unstructured outdoor environment. Next, we focus on learning a model to predict instances of inter-layer conflict, and planning to act with respect to this model. This is demonstrated using supervised learning in a physics-based simulation environment. Results and algorithms are presented.
---
paper_title: Assessing cooperation in human control of heterogeneous robots

paper_content:

Human control of multiple robots has been characterized by the average demand of single robots on human attention. While this matches situations in which independent robots are controlled sequentially it does not capture aspects of demand associated with coordinating dependent actions among robots. This paper presents an extension of Crandall's neglect tolerance model intended to accommodate both coordination demands (CD) and heterogeneity among robots. The reported experiment attempts to manipulate coordination demand by varying the proximity needed to perform a joint task in two conditions and by automating coordination within subteams in a third. Team performance and the process measure CD were assessed for each condition. Automating cooperation reduced CD and improved performance. We discuss the utility of process measures such as CD to analyze and improve control performance.
---
paper_title: Immunological Computation: Theory and Applications

paper_content:

Over the last decade, the field of immunological computation has progressed slowly and steadily as a branch of computational intelligence. Immunological Computation: Theory and Applications presents up-to-date immunity-based computational techniques. After a brief review of fundamental immunology concepts, the book presents computational models based on the negative selection process that occurs in the thymus. It then examines immune networks, including continuous and discrete immune network models, clonal selection, hybrid models, and computational models based on danger theory. The book also discusses real-world applications for all of the models covered in each chapter.
---
paper_title: Evolving Mobile Robots Able to Display Collective Behaviors

paper_content:

We present a set of experiments in which simulated robots are evolved for the ability to aggregate and move together toward a light target. By developing and using quantitative indexes that capture the structural properties of the emerged formations, we show that evolved individuals display interesting behavioral patterns in which groups of robots act as a single unit. Moreover, evolved groups of robots with identical controllers display primitive forms of situated specialization and play different behavioral functions within the group according to the circumstances. Overall, the results presented in the article demonstrate that evolutionary techniques, by exploiting the self-organizing behavioral properties that emerge from the interactions between the robots and between the robots and the environment, are a powerful method for synthesizing collective behavior.
---
paper_title: Evolving Homogeneous Neurocontrollers for a Group of Heterogeneous Robots: Coordinated Motion, Cooperation, and Acoustic Communication

paper_content:

This article describes a simulation model in which artificial evolution is used to design homogeneous control structures and adaptive communication protocols for a group of three autonomous simulated robots. The agents are required to cooperate in order to approach a light source while avoiding collisions. The robots are morphologically different: Two of them are equipped with infrared sensors, one with light sensors. Thus, the two morphologically identical robots should take care of obstacle avoidance; the other one should take care of phototaxis. Since all of the agents can emit and perceive sound, the group's coordination of actions is based on acoustic communication. The results of this study are a proof of concept: They show that dynamic artificial neural networks can be successfully synthesized by artificial evolution to design the neural mechanisms required to underpin the behavioral strategies and adaptive communication capabilities demanded by this task. Postevaluation analyses unveil operational aspects of the best evolved behavior. Our results suggest that the building blocks and the evolutionary machinery detailed in the article should be considered in future research work dealing with the design of homogeneous controllers for groups of heterogeneous cooperating and communicating robots.
---
paper_title: Decentralized Cooperative Policy for Conflict Resolution in Multivehicle Systems

paper_content:

In this paper, we propose a novel policy for steering multiple vehicles between assigned start and goal configurations, ensuring collision avoidance. The policy rests on the assumption that all agents are cooperating by implementing the same traffic rules. However, the policy is completely decentralized, as each agent decides its own motion by applying those rules only on the locally available information, and scalable, in the sense that the amount of information processed by each agent and the computational complexity of the algorithms do not increase with the number of agents in the scenario. The proposed policy applies to systems in which new vehicles may enter the scene and start interacting with existing ones at any time, while others may leave. Under mild conditions on the initial configurations, the policy is shown to be safe, i.e., it guarantees collision avoidance throughout the system evolution. In the paper, conditions are discussed on the desired configurations of agents, under which the ultimate convergence of all vehicles to their goals can also be guaranteed. To show that such conditions are actually necessary and sufficient, which turns out to be a challenging liveness-verification problem for a complex hybrid automaton, we employ a probabilistic verification method. The paper finally presents and discusses simulations for systems of several tens of vehicles, and reports on some experimental implementation showing the practicality of the approach.
---
paper_title: An overview of planning technology in robotics

paper_content:

We present here an overview of several planning techniques in robotics. We will not be concerned with the synthesis of abstract mission and task plans, using well known classical and other domain-independent planning techniques. We will mainly focus on to how refine such abstract plans into robust sensory-motor actions and on some planning techniques that can be useful for that.
---
paper_title: A theory of self-nonself discrimination.

paper_content:

1) Induction of humoral antibody formation involves the obligatory recognition of two determinants on an antigen, one by the receptor antibody of the antigen-sensitive cell and the other by carrier antibody (associative interaction). 2) Paralysis of antibody formation involves the obligatory recognition of only one determinant by the receptor antibody of the antigen-sensitive cell; that is, a nonimmunogenic molecule (a hapten) can paralyze antigen-sensitive cells. 3) There is competition between paralysis and induction at the level of the antigen-sensitive cell. 4) The mechanisms of low- and high-zone paralysis, and maintenance of the unresponsive state, are identical. 5) High-zone paralysis occurs when both the carrier antibody and the receptor antibody are saturated, so that associated interactions cannot take place. 6) The mechanisms of paralysis and induction for the carrier-antigen-sensitive cell are identical to those for the humoral-antigen-sensitive cell. 7) The formation of carrier-antigen-sensitive cells is thymus-dependent, whereas humoral-antigen-sensitive cells are derived from bone marrow. Since carrier antibody is required for induction, all antigens are thymus-dependent. 8) The interaction of antigen with the receptor antibody on an antigen-sensitive cell results in a conformational change in an invariant region of the receptor and consequently paralyzes the cell. As the receptor is probably identical to the induced antibody, all antibody molecules are expected to be able to undergo a conformational change on binding a hapten. The obligatory associated recognition by way of carrier antibody (inductive signal) involves a conformational change in the carrier antibody, leading to a second signal to the antigen-sensitive cell. 9) The foregoing requirements provide an explanation for self-nonself discrimination. Tolerance to self-antigens involves a specific deletion in the activity of both the humoral- and the carrier-antigen-sensitive cells.
---
paper_title: The danger model: A renewed sense of self

paper_content:

For over 50 years immunologists have based their thoughts, experiments, and clinical treatments on the idea that the immune system functions by making a distinction between self and nonself. Although this paradigm has often served us well, years of detailed examination have revealed a number of inherent problems. This Viewpoint outlines a model of immunity based on the idea that the immune system is more concerned with entities that do damage than with those that are foreign.
---
paper_title: The danger model: A renewed sense of self

paper_content:

For over 50 years immunologists have based their thoughts, experiments, and clinical treatments on the idea that the immune system functions by making a distinction between self and nonself. Although this paradigm has often served us well, years of detailed examination have revealed a number of inherent problems. This Viewpoint outlines a model of immunity based on the idea that the immune system is more concerned with entities that do damage than with those that are foreign.
---
paper_title: A Paratope is Not an Epitope: Implications for Immune Network Models and Clonal Selection

paper_content:

Artificial Immune Systems (AIS) research into clonal selection and immune network models has tended to use a single, real-valued or binary vector to represent both the paratope and epitope of a B-cell; in this paper, the use of alternative representations is discussed. A theoretical generic immune network (GIN) is presented, that can be used to explore the network dynamics of several families of different B-cell representations at the same time, and that combines features of clonal selection and immune networks in a single model.
---
paper_title: Improved Pattern Recognition with Artificial Clonal Selection?

paper_content:

In this paper, we examine the clonal selection algorithm CLONALG and the suggestion that it is suitable for pattern recognition. CLONALG is tested over a series of binary character recognition tasks and its performance compared to a set of basic binary matching algorithms. A number of enhancements are made to the algorithm to improve its performance and the classification tests are repeated. Results show that given enough data CLONALG can successfully classify previously unseen patterns and that adjustments to the existing algorithm can improve performance.
---
paper_title: Immunological Computation: Theory and Applications

paper_content:

Over the last decade, the field of immunological computation has progressed slowly and steadily as a branch of computational intelligence. Immunological Computation: Theory and Applications presents up-to-date immunity-based computational techniques. After a brief review of fundamental immunology concepts, the book presents computational models based on the negative selection process that occurs in the thymus. It then examines immune networks, including continuous and discrete immune network models, clonal selection, hybrid models, and computational models based on danger theory. The book also discusses real-world applications for all of the models covered in each chapter.
---
paper_title: The immune system, adaptation, and machine learning

paper_content:

Abstract The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.
---
paper_title: Stability of symmetric idiotypic networks—A critique of Hoffmann's analysis

paper_content:

Hoffmann (1982) analysed a very simple model of suppressive idiotypic immune networks and showed that idiotypic interactions are stabilizing. He concluded that immune networks provide a counterexample to the general analysis of large dynamic systems (Gardner and Ashby, 1970; May, 1972). The latter is often verbalized as: an increase in size and/or connectivity decreases the system stability. We here analyse this apparent contradiction by extending the Hoffmann model (with a decay term), and comparing it to an ecological model that was used as a paradigm in the general analysis. Our analysis confirms that the neighbourhood stability of such idiotypic networks increases with connectivity and/or size. However, the contradiction is one of interpretation, and is not due to exceptional properties of immune networks. The contradiction is caused by the awkward normalization used in the general analysis.
---
paper_title: The 'complete' idiotype network is an absurd immune system.

paper_content:

Idiotypic networks have attained the status of unavoidable necessities in the regulation of immune responses. In this article Rod Langman and Mel Cohn contend that the conceptual foundations for such idiotypic networks are formal absurdities.
---
paper_title: Integrated Innate and Adaptive Artificial Immune Systems Applied to Process Anomaly Detection

paper_content:

This thesis explores the design and application of artificial immune systems (AISs), problem-solving systems inspired by the human and other immune systems. AISs to date have largely been modelled on the biological adaptive immune system and have taken little inspiration from the innate immune system. The first part of this thesis examines the biological innate immune system, which controls the adaptive immune system. The importance of the innate immune system suggests that AISs should also incorporate models of the innate immune system as well as the adaptive immune system. This thesis presents and discusses a number of design principles for AISs which are modelled on both innate and adaptive immunity. These novel design principles provided a structured framework for developing AISs which incorporate innate and adaptive immune systems in general. These design principles are used to build a software system which allows such AISs to be implemented and explored. AISs, as well as being inspired by the biological immune system, are also built to solve problems. In this thesis, using the software system and design principles we have developed, we implement several novel AISs and apply them to the problem of detecting attacks on computer systems. These AISs monitor programs running on a computer and detect whether the program is behaving abnormally or being attacked. The development of these AISs shows in more detail how AISs built on the design principles can be instantiated. In particular, we show how the use of AISs which incorporate both innate and adaptive immune system mechanisms can be used to reduce the number of false alerts and improve the performance of current approaches.
---
paper_title: Articulation and Clarification of the Dendritic Cell Algorithm

paper_content:

The Dendritic Cell algorithm (DCA) is inspired by recent work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of computer security. A port scan detection task is performed to substantiate the influence of signal selection on the behaviour of the algorithm. Experimental results provide a comparison of differing input signal mappings.
---
paper_title: Danger Theory: The Link between AIS and IDS?

paper_content:

We present ideas about creating a next generation Intrusion Detection System based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems. The Human Immune System can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System for our computers.
---
paper_title: An immunological approach to mobile robot reactive navigation

paper_content:

In this paper, a reactive immune network (RIN) is proposed and employed for mobile robot navigation within unknown environments. Rather than building a detailed mathematical model of artificial immune systems, this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback. In addition, an adaptive virtual target method is integrated to solve the local minima problem in navigation. Several trapping situations designed by the early researchers are adopted to evaluate the performance of the proposed architecture. Simulation results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching the goal efficiently and effectively.
---
paper_title: Immunity-based autonomous guided vehicles control

paper_content:

The human immune system is a self-organizing and highly distributed multi-agent system. These properties impart a high degree of robustness and performance that has created great interest in implementing engineering systems. This adopted engineering analogue is called Artificial Immune System (AIS). This paper presents an immunity-based control framework which has the ability to detect changes, adapt to dynamic environment and coordinate vehicles activities for goals achievement, to deploy a fleet of AGVs for material handling in an automated warehouse. A robust and flexible automated warehousing system is achieved through the non-deterministic and fully decentralized origination of AGVs.
---
paper_title: Modeling a MultiAgent Mobile Robotics Test Bed Using a Biologically Inspired Artificial Immune System

paper_content:

The biological immune system is a complex, adaptive, pattern-recognition system that defends the body from foreign pathogens. The system uses learning, memory, and associative retrieval to solve recognition issues and classification of tasks. In particular, it learns to recognize relevant problems, remember those encountered in the past, and uses combinations to construct problem detectors efficiently. This paper explores an application of an adaptive learning mechanism for robots based on the natural immune system, using two algorithms, viz., the behavior arbitration mechanism and the clonal selection algorithm to demonstrate the innate and adaptive immune response respectively. The work highlights the innate and adaptive characteristics of the immune system, wherein a robot learns to detect vulnerable areas of a track and adapts to the required speed over such portions. A detailed study of the artificial immune metaphor is carried out and mapped onto the robot world. The robotics test bed comprised of two Lego robots deployed simultaneously on two predefined near concentric tracks with the outer robot capable of helping the inner one when it misaligns. The inner robot raises an SOS signal on misalignment. The outer robot aids the inner robot to regain it alignment exhibiting the innate immunity. The adaptive system within the inner robot learns to tackle the problem in future using Clonal Selection mechanism.
---
paper_title: Clonal selection based mobile robot path planning

paper_content:

Clonal selection based mobile robot global path planning method is presented in the article, which is composed of encoding of antibody, fitness function construct, selection strategy and immune operator definition. Path distance and degree of intersecting with obstacle are both considered in the definition of fitness function. Mutation operator, insert operator and delete operator are designed according to the problem of mobile robot path planning. Mutation is different in genetic algorithm (GA) from in clonal selection, In the GA, mutation aims improving diversity of population, while in the clonal selection, mutation aims accelerating convergence of algorithm. The algorithm proposed in the paper spends less time and have better result of path planning than GA. The efficiency of proposed method is validated by simulation with MATLAB.
---
paper_title: A hybrid immune evolutionary computation based on immunity and clonal selection for concurrent mapping and localization

paper_content:

This paper addresses the problem of Concurrent Mapping and Localization(CML) by means of a hybrid immune evolutionary computation based on immunity and clonal selection for a mobile robot. An immune operator, a vaccination operator, is designed in the algorithm. The experiment results of a real mobile robot show that the computational expensiveness of the algorithm in this paper is less than other algorithms and the maps obtained are very accurate.
---
paper_title: An evolutionary algorithm with population immunity and its application on autonomous robot control

paper_content:

The natural immune system is an important resource full of inspirations for the theory researchers and the engineering developers to design some powerful information processing methods aiming at difficult problems. Based on this consideration, a novel optimal-searching algorithm, the immune mechanism based evolutionary algorithm - IMEA, is proposed for the purpose of finding an optimal/quasi-optimal solution in a multi-dimensional space. Different from the ordinary evolutionary algorithms, on one hand, due to the long-term memory, IMEA has a better capability of learning from its experience, and on the other hand, with the clonal selection, it is able to keep from the premature convergence of population. With the simulation on autonomous robot control, it is proved that IMEA is good at the task of adaptive adjustment (offline), and it can improve the robot's capability of reinforcement learning, so as to make itself able to sense its surrounding dynamic environment.
---
paper_title: Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system

paper_content:

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in a distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. To apply the immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robots using communication. Finally, the most stimulated strategy is adopted as the swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. It is used for decision making of the optimal swarm strategy. By T-cell modeling, the adaptation ability of the robot is enhanced in dynamic environments.
---
paper_title: Immunoid: An Immunological Approach to Decentralized Behavoir Arbitration of Autonomous Mobile Robots

paper_content:

Conventional artificial intelligent (AI) system have been criticized for its brittleness under hostile/dynamic changing environments. Therefore, recently much attention has been focused on the reactive planning systems such as behavior-based AI. However, in the behavior-based AI approaches, how to construct a mechanism that realizes adequate arbitration among competence modules is still an open question. In this paper, we propose a new decentralized consensus-making system inspired from the biological immune system. And we apply our proposed method to behavior arbitration of an autonomous mobile robot as a practical example. To verify the feasibility of our method, we carry out some experiments. In addition, we propose an adaptation mechanism, and try to construct a suitable immune network for adequate action selection.
---
paper_title: Emergent construction of behavior arbitration mechanism based on the immune system

paper_content:

We have been investigating a new behavior arbitration mechanism based on the biological immune system. The behavior arbitration mechanism and the biological immune system share certain similarities since both systems deal with various sensory inputs (antigens) through interactions among multiple competence modules (lymphocytes and/or antibodies). We have demonstrated the flexible arbitration abilities of our proposed method, however, we have not previously shown a solution to the problem: how do we prepare an appropriate repertoire of competence modules? In this paper, in order to construct an appropriate immune network without human intervention, we try to incorporate an off-line metadynamics function into our previously proposed mechanism. The metadynamics function is an adaptation realized by varying the structure of the immune network. To accomplish this function, we use a genetic algorithm with a devised crossover operator. Finally, we verify our method by carrying out simulations.
---
paper_title: Decentralized control system for autonomous navigation based on an evolved artificial immune network

paper_content:

This paper investigates an autonomous control system of a mobile robot based on the immune network theory. The immune network navigates the robot to solve a multiobjective task, namely, garbage collection: the robot must find and collect garbage, while it establishes a trajectory without colliding with obstacles, and return to the base before it runs out of energy. Each network node corresponds to a specific antibody and describes a particular control action for the robot. The antigens are the current state of the robot, read from a set of internal and external sensors. The network dynamics corresponds to the variation of antibody concentration levels, which change according to both mutual interaction of antibody nodes and of antibodies and antigens. It is proposed an evolutionary mechanism to determine the network configuration, that is, the parameters that define those interactions. Simulation results suggest that the proposal presented is very promising.
---
paper_title: Plan on Obstacle-Avoiding Path for Mobile Robots Based on Artificial Immune Algorithm

paper_content:

This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle; AIA has a strong parallel processing, learning and memorizing ability. This study will design and control a mobile robot within a limited special scale. Through a research method based on the AIA, this study will find out the optimum obstacle-avoiding path. The main purpose of this study is to make it possible for the mobile robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle and best learning efficiency. In the end, through the research method proposed and the experimental results, it will become obvious that the application of the AIA after improvement in the obstacle-avoiding path planning for mobile robots is really effective.
---
paper_title: A robot with a decentralized consensus-making mechanism based on the immune system

paper_content:

In recent years much attention has been focused on behavior-based artificial intelligence (AI), which has already demonstrated its robustness and flexibility against dynamically changing world. However, in this approach, the followings have not yet been resolved: how do we construct an appropriate arbitration mechanism, and how do we prepare appropriate competence modules. In this paper, to overcome these problems, we propose a new decentralised consensus-making system inspired by the biological immune system. And we apply our proposed method to behavior arbitration for an autonomous mobile robot, namely garbage collecting problem that takes into account the concept of self-sufficiency. To verify the feasibility of our method, we carry out some simulations. In addition, we investigate two types of adaptation mechanisms, and try to evolve the proposed artificial immune network using reinforcement signals.
---
paper_title: Immune network control for stigmergy based foraging behaviour of autonomous mobile robots

paper_content:

The paper presents a series of experiments in a simulated environment where two autonomous mobile robots gather randomly distributed objects and cluster them on a pile. The co-ordination of the robots' movements is achieved through stigmergy (an indirect form of communication through the environment). The random moves, necessary for stigmergy based foraging behaviour, make the task solution a time consuming process. In order to speed up the foraging behaviour, the immune network robot control is proposed. Stigmergic principles are coded in two artificial immune networks—for a collision free goal following behaviour and for an object picking up/dropping behaviour. Simulations confirm the improved performance of the foraging behaviour under the proposed immune network control. Copyright © 2006 John Wiley & Sons, Ltd.
---
paper_title: Artificial Immune System based Cooperative Strategies for Robot Soccer Competition

paper_content:

This study proposes an immune network based cooperative strategy for robot soccer systems. The strategy enables robots to select proper behaviors from `shot', `pass', `kick', `chase', `track', and `guard'. In addition, the proposed layered immune network achieves cooperation and coordination between each robot. The proposed architecture is evaluated on the SimuroSot Middle league, a 5-vs-5 simulation platform in FIRA.
---
paper_title: An immune learning classifier network for autonomous navigation

paper_content:

This paper proposes a non-parametric hybrid system for autonomous navigation combining the strengths of learning classifier systems, evolutionary algorithms, and an immune network model. The system proposed is basically an immune network of classifiers, named CLARINET. CLARINET has three degrees of freedom: the attributes that define the network cells (classifiers) are dynamically adjusted to a changing environment; the network connections are evolved using an evolutionary algorithm; and the concentration of network nodes is varied following a continuous dynamic model of an immune network. CLARINET is described in detail, and the resultant hybrid system demonstrated effectiveness and robustness in the experiments performed, involving the computational simulation of robotic autonomous navigation.
---
paper_title: AIS Based Robot Navigation in a Rescue Scenario

paper_content:

An architecture for a robot control is proposed which is based on the requirements from the RoboCup and AAAI Rescue Robot Competition. An artificial immune system comprises the core component. The suitability of this architecture for the competition and related scenarios, including the modelling of the environment, was verified by simulation.
---
paper_title: The danger model: A renewed sense of self

paper_content:

For over 50 years immunologists have based their thoughts, experiments, and clinical treatments on the idea that the immune system functions by making a distinction between self and nonself. Although this paradigm has often served us well, years of detailed examination have revealed a number of inherent problems. This Viewpoint outlines a model of immunity based on the idea that the immune system is more concerned with entities that do damage than with those that are foreign.
---
paper_title: Two-Timescale Learning Using Idiotypic Behaviour Mediation For A Navigating Mobile Robot

paper_content:

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile-robot navigation problems is presented and tested in both the real and virtual domains. The LTL phase consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours, encoded as variable sets of attributes, and the STL phase is an idiotypic Artificial Immune System. Results from the LTL phase show that sets of behaviours develop very rapidly, and significantly greater diversity is obtained when multiple autonomous populations are used, rather than a single one. The architecture is assessed under various scenarios, including removal of the LTL phase and switching off the idiotypic mechanism in the STL phase. The comparisons provide substantial evidence that the best option is the inclusion of both the LTL phase and the idiotypic system. In addition, this paper shows that structurally different environments can be used for the two phases without compromising transferability.
---
paper_title: An immune learning classifier network for autonomous navigation

paper_content:

This paper proposes a non-parametric hybrid system for autonomous navigation combining the strengths of learning classifier systems, evolutionary algorithms, and an immune network model. The system proposed is basically an immune network of classifiers, named CLARINET. CLARINET has three degrees of freedom: the attributes that define the network cells (classifiers) are dynamically adjusted to a changing environment; the network connections are evolved using an evolutionary algorithm; and the concentration of network nodes is varied following a continuous dynamic model of an immune network. CLARINET is described in detail, and the resultant hybrid system demonstrated effectiveness and robustness in the experiments performed, involving the computational simulation of robotic autonomous navigation.
---
paper_title: Genetic-Algorithm Seeding Of Idiotypic Networks For Mobile-Robot Navigation

paper_content:

Robot-control designers have begun to exploit the properties of the human immune system in order to ::: produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.
---
paper_title: Artificial immune network-based cooperative control in collective autonomous mobile robots

paper_content:

In this paper, we propose a method of cooperative control based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying the immune system to DARS, a robot is regarded as a B lymphocyte (B cell), each environmental condition as an antigen and a behavior strategy as an antibody respectively. The executing process of proposed method is as follows: when the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robot using communication. Finally, such stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis, and it is used for decision making of an optimal swarm strategy.
---
paper_title: Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system

paper_content:

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on the immune system in a distributed autonomous robotic system (DARS). The immune system is a living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in a dynamically changing environment. To apply the immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition changes, a robot selects an appropriate behavior strategy, and its behavior strategy is stimulated and suppressed by other robots using communication. Finally, the most stimulated strategy is adopted as the swarm behavior strategy. This control scheme is based on clonal selection and idiotopic network hypothesis. It is used for decision making of the optimal swarm strategy. By T-cell modeling, the adaptation ability of the robot is enhanced in dynamic environments.
---
paper_title: Applying synthesized immune networks hypothesis to mobile robots

paper_content:

Based on the analogies between multi autonomous robots system (MARS) and immune system, a synthesized immune networks hypothesis and the algorithm to solve the route planning and cooperation problem on MARS are proposed. The route planning and cooperation problem is transformed to the interaction mechanism among antibody, antigen and small-scaled immune networks. The pursuit problem is used to validate the hypothesis. Simulation results suggest that the proposal is promising.
---
paper_title: Plan on Obstacle-Avoiding Path for Mobile Robots Based on Artificial Immune Algorithm

paper_content:

This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle; AIA has a strong parallel processing, learning and memorizing ability. This study will design and control a mobile robot within a limited special scale. Through a research method based on the AIA, this study will find out the optimum obstacle-avoiding path. The main purpose of this study is to make it possible for the mobile robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle and best learning efficiency. In the end, through the research method proposed and the experimental results, it will become obvious that the application of the AIA after improvement in the obstacle-avoiding path planning for mobile robots is really effective.
---
paper_title: An immunological approach to mobile robot reactive navigation

paper_content:

In this paper, a reactive immune network (RIN) is proposed and employed for mobile robot navigation within unknown environments. Rather than building a detailed mathematical model of artificial immune systems, this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback. In addition, an adaptive virtual target method is integrated to solve the local minima problem in navigation. Several trapping situations designed by the early researchers are adopted to evaluate the performance of the proposed architecture. Simulation results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching the goal efficiently and effectively.
---
paper_title: Behavior-based intelligent mobile robot using an immunized reinforcement adaptive learning mechanism

paper_content:

Abstract In this paper, a novel immunized reinforcement adaptive learning mechanism employing a behavior-based knowledge and the on-line adapting capabilities of the immune system is proposed and applied to an intelligent mobile robot. Rather than building a detailed mathematical model of immune systems, we try to explore principles in the immune system focusing on its self-organization, adaptive capability and immune memory. Two levels of the immune system, underlying the ‘micro’ level of cell interactions, and emergent ‘macro’ level of the behavior of the system are investigated. To evaluate the proposed immunized architecture, a ‘food foraging work’ simulation environment containing a mobile robot, foods, with/without obstacles is created to simulate the real world. The simulation results validate several significant characteristics of the immunized architecture: adaptability, learning, self-organizing, and stable ecological niche approaching.
---
paper_title: Artificial Immune System based Cooperative Strategies for Robot Soccer Competition

paper_content:

This study proposes an immune network based cooperative strategy for robot soccer systems. The strategy enables robots to select proper behaviors from `shot', `pass', `kick', `chase', `track', and `guard'. In addition, the proposed layered immune network achieves cooperation and coordination between each robot. The proposed architecture is evaluated on the SimuroSot Middle league, a 5-vs-5 simulation platform in FIRA.
---
paper_title: Mobile Robot Path Planning Based on Artificial Immune Algorithm

paper_content:

* Corresponding author Email: aulimin@126.com Abstract - This paper studies the application of artificial immune algorithm to mobile robot path planning inside a specified environment in real time. The biological immune system is firstly analyzed in a relatively deeper and all-sided point of view reflecting the fresh research in biology. Second, the motion characteristic of the car-like autonomous mobile robot is also analyzed. An immunity algorithm adapting capabilities of the artificial immune system is proposed and enable robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle efficiency. Finally, the simulation experiment results demonstrate that the proposed AIA based path plan approach behaves more successfully.
---
paper_title: Research on Cooperative Strategies of Soccer Robots Based on Artificial Immune System

paper_content:

For one agent, the proper selection of its own actions or strategies is one key to the multi-agent system. A new method is presented to select actions cooperatively for soccer robots, which is regarded as a benchmark of multi-agent system. The perfect character of artificial immune system is made full use to improve the strategy of the soccer robot, dealing with information of the environment as antigen, certain action as antibody. Like biological immune system, the robot selects action (antibody) according to its own surroundings (antigen) to make robots complete their mission properly and cooperatively. Experiment results indicate the feasibility and effectiveness of the method.
---
paper_title: An artificial immune system approach to mobile sensor networks and mine detection

paper_content:

The human immune system and its ability to continually develop and learn has been studied for many years. Its capacity to recognize its own cells (self), as well as those that pose a threat to the body's homeostasis (non-self) has lead to the creation of many algorithms that try to mimic this behavior. One of these algorithms is the artificial immune system (AIS). The foundation of AIS depends on the existence of both sensor and communication ranges (assumed circles) with which a single agent can collect and convey information to other agents within the range. It is the purpose of this paper to present a solution to the mine detection problem using cooperative mobile robots equipped with sensory and communication capabilities which allow the AIS approach to be utilized
---
paper_title: Application of artificial immune system based intelligent multi agent model to a mine detection problem

paper_content:

Biological systems are sophisticated and intelligent information processing systems. They have inspired scientists and engineers to solve complex computational and information processing tasks. This paper presents a novel artificial immune system based intelligent multi agent model named AISIMAM. The model involves the behavioral management of artificial intelligence, namely multi agent systems and artificial immune systems. This paper outlines AISIMAM, concentrates on the mine detection application and discusses the results.
---
paper_title: Control of the distributed autonomous robotic system based on the biologically inspired immunological architecture

paper_content:

In this paper, we propose a new algorithm to control a distributed autonomous robotic system under dynamically changing environment based on immunological interaction between robots. Our algorithm can organize the robot population for dynamically changing multiple works. At first, we designed a control architecture for a multiple robotic system based on B-cell (which is main agent of immune system) interaction. Immune system has various kinds of B-cells, and B-cell interaction can organize its population balance against dynamically changing environment. We set the analogy between distributed autonomous robotic system and biological immune system. We verified the performance of our algorithm by computer simulation. As a simulation example there is considered the transportation of multiple objects to multiple locations against a deadline, with time-varying demand.
---
paper_title: Artificial Dendritic Cells: Multi-faceted Perspectives

paper_content:

Dendritic cells are the crime scene investigators of the human immune system. Their function is to correlate potentially anomalous invading entities with observed damage to the body. The detection of such invaders by dendritic cells results in the activation of the adaptive immune system, eventually leading to the removal of the invader from the host body. This mechanism has provided inspiration for the development of a novel bio-inspired algorithm, the Dendritic Cell Algorithm. This algorithm processes information at multiple levels of resolution, resulting in the creation of information granules of variable structure. In this chapter we examine the multi-faceted nature of immunology and how research in this field has shaped the function of the resulting Dendritic Cell Algorithm. A brief overview of the algorithm is given in combination with the details of the processes used for its development. The chapter is concluded with a discussion of the parallels between our understanding of the human immune system and how such knowledge influences the design of artificial immune systems.
---
paper_title: A goalkeeper strategy in robot soccer based on Danger Theory

paper_content:

Artificial Immune Systems (AIS) have been successfully modeled and implemented in several engineering applications. In this work, a goalkeeper strategy in robot soccer based on Danger Theory is proposed. Danger Theory is a recent immune theory which has not been widely applied so far. The proposed strategy is implemented and evaluated using middle league SIMUROSOT from FIRA. Experiments carried out yielded promising results.
---
paper_title: Articulation and Clarification of the Dendritic Cell Algorithm

paper_content:

The Dendritic Cell algorithm (DCA) is inspired by recent work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of computer security. A port scan detection task is performed to substantiate the influence of signal selection on the behaviour of the algorithm. Experimental results provide a comparison of differing input signal mappings.
---
paper_title: Danger Theory: The Link between AIS and IDS?

paper_content:

We present ideas about creating a next generation Intrusion Detection System based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems. The Human Immune System can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System for our computers.
---
paper_title: The Deterministic Dendritic Cell Algorithm

paper_content:

The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.
---
paper_title: The Application of a Dendritic Cell Algorithm to a Robotic Classifier

paper_content:

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.
---
paper_title: Multi-robot Exploration Based on Market Approach and Immune Optimizing Strategy

paper_content:

A multi-robot system is used to explore an environment and create a map based on market approach. Data fusion is performed using Bayes theorem and then the local maps are updated. A diffusivity concept is defined to describe the robots' extent apart from one another. The immune optimizing strategy is introduced to select goal points since it is a problem of optimized combination. In order to minimize repeated coverage and improve the exploration efficiency, the evaluation function considers the cost, revenue and diffusivity. Simulation examples show that the proposed method is effective for the stated problem and the immune optimizing strategy is more efficient than other strategies.
---
paper_title: Real-time obstacle avoidance for manipulators and mobile robots

paper_content:

This paper presents a unique real-time obstacle avoidance approach for manipulators and mobile robots based on the "artificial potential field" concept. In this approach, collision avoidance, traditionally considered a high level planning problem, can be effectively distributed between different levels of control, allowing real-time robot operations in a complex environment. We have applied this obstacle avoidance scheme to robot arm using a new approach to the general problem of real-time manipulator control. We reformulated the manipulator control problem as direct control of manipulator motion in operational space-the space in which the task is originally described-rather than as control of the task's corresponding joint space motion obtained only after geometric and kinematic transformation. This method has been implemented in the COSMOS system for a PUMA 560 robot. Using visual sensing, real-time collision avoidance demonstrations on moving obstacles have been performed.
---
paper_title: Decentralized control system for autonomous navigation based on an evolved artificial immune network

paper_content:

This paper investigates an autonomous control system of a mobile robot based on the immune network theory. The immune network navigates the robot to solve a multiobjective task, namely, garbage collection: the robot must find and collect garbage, while it establishes a trajectory without colliding with obstacles, and return to the base before it runs out of energy. Each network node corresponds to a specific antibody and describes a particular control action for the robot. The antigens are the current state of the robot, read from a set of internal and external sensors. The network dynamics corresponds to the variation of antibody concentration levels, which change according to both mutual interaction of antibody nodes and of antibodies and antigens. It is proposed an evolutionary mechanism to determine the network configuration, that is, the parameters that define those interactions. Simulation results suggest that the proposal presented is very promising.
---
paper_title: Plan on Obstacle-Avoiding Path for Mobile Robots Based on Artificial Immune Algorithm

paper_content:

This paper aims to plan the obstacle-avoiding path for mobile robots based on the Artificial Immune Algorithm (AIA) developed from the immune principle; AIA has a strong parallel processing, learning and memorizing ability. This study will design and control a mobile robot within a limited special scale. Through a research method based on the AIA, this study will find out the optimum obstacle-avoiding path. The main purpose of this study is to make it possible for the mobile robot to reach the target object safely and successfully fulfill its task through optimal path and with minimal rotation angle and best learning efficiency. In the end, through the research method proposed and the experimental results, it will become obvious that the application of the AIA after improvement in the obstacle-avoiding path planning for mobile robots is really effective.
---
paper_title: A robot with a decentralized consensus-making mechanism based on the immune system

paper_content:

In recent years much attention has been focused on behavior-based artificial intelligence (AI), which has already demonstrated its robustness and flexibility against dynamically changing world. However, in this approach, the followings have not yet been resolved: how do we construct an appropriate arbitration mechanism, and how do we prepare appropriate competence modules. In this paper, to overcome these problems, we propose a new decentralised consensus-making system inspired by the biological immune system. And we apply our proposed method to behavior arbitration for an autonomous mobile robot, namely garbage collecting problem that takes into account the concept of self-sufficiency. To verify the feasibility of our method, we carry out some simulations. In addition, we investigate two types of adaptation mechanisms, and try to evolve the proposed artificial immune network using reinforcement signals.
---
paper_title: An immune learning classifier network for autonomous navigation

paper_content:

This paper proposes a non-parametric hybrid system for autonomous navigation combining the strengths of learning classifier systems, evolutionary algorithms, and an immune network model. The system proposed is basically an immune network of classifiers, named CLARINET. CLARINET has three degrees of freedom: the attributes that define the network cells (classifiers) are dynamically adjusted to a changing environment; the network connections are evolved using an evolutionary algorithm; and the concentration of network nodes is varied following a continuous dynamic model of an immune network. CLARINET is described in detail, and the resultant hybrid system demonstrated effectiveness and robustness in the experiments performed, involving the computational simulation of robotic autonomous navigation.
---
paper_title: An Information-Theoretic Approach for Clonal Selection Algorithms

paper_content:

In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms. The obtained results show as i-CSA is effective in terms of accuracy, and it is able to solve large-scale instances of well-known benchmarks. Experimental results also indicate that the algorithm is comparable, and often outperforms, the compared nature-inspired approaches. From the experimental results, it is possible to note that a longer maturation of a B cell, inside the population, assures the achievement of better solutions; the maturation period affects the diversity and the effectiveness of the immune search process on a specific problem instance. To assess the learning capability during the evolution of the algorithm three different relative entropies were used: Kullback-Leibler, Renyi generalized and Von Neumann divergences. The adopted entropic divergences show a strong correlation between optima discovering, and high relative entropy values.
---
paper_title: A hybrid immune evolutionary computation based on immunity and clonal selection for concurrent mapping and localization

paper_content:

This paper addresses the problem of Concurrent Mapping and Localization(CML) by means of a hybrid immune evolutionary computation based on immunity and clonal selection for a mobile robot. An immune operator, a vaccination operator, is designed in the algorithm. The experiment results of a real mobile robot show that the computational expensiveness of the algorithm in this paper is less than other algorithms and the maps obtained are very accurate.
---
paper_title: An evolutionary algorithm with population immunity and its application on autonomous robot control

paper_content:

The natural immune system is an important resource full of inspirations for the theory researchers and the engineering developers to design some powerful information processing methods aiming at difficult problems. Based on this consideration, a novel optimal-searching algorithm, the immune mechanism based evolutionary algorithm - IMEA, is proposed for the purpose of finding an optimal/quasi-optimal solution in a multi-dimensional space. Different from the ordinary evolutionary algorithms, on one hand, due to the long-term memory, IMEA has a better capability of learning from its experience, and on the other hand, with the clonal selection, it is able to keep from the premature convergence of population. With the simulation on autonomous robot control, it is proved that IMEA is good at the task of adaptive adjustment (offline), and it can improve the robot's capability of reinforcement learning, so as to make itself able to sense its surrounding dynamic environment.
---
paper_title: Improved Pattern Recognition with Artificial Clonal Selection?

paper_content:

In this paper, we examine the clonal selection algorithm CLONALG and the suggestion that it is suitable for pattern recognition. CLONALG is tested over a series of binary character recognition tasks and its performance compared to a set of basic binary matching algorithms. A number of enhancements are made to the algorithm to improve its performance and the classification tests are repeated. Results show that given enough data CLONALG can successfully classify previously unseen patterns and that adjustments to the existing algorithm can improve performance.
---
paper_title: Theoretical advances in artificial immune systems

paper_content:

Artificial immune systems (AIS) constitute a relatively new area of bio-inspired computing. Biological models of the natural immune system, in particular the theories of clonal selection, immune networks and negative selection, have provided the inspiration for AIS algorithms. Moreover, such algorithms have been successfully employed in a wide variety of different application areas. However, despite these practical successes, until recently there has been a dearth of theory to justify their use. In this paper, the existing theoretical work on AIS is reviewed. After the presentation of a simple example of each of the three main types of AIS algorithm (that is, clonal selection, immune network and negative selection algorithms respectively), details of the theoretical analysis for each of these types are given. Some of the future challenges in this area are also highlighted.
---
paper_title: The 'complete' idiotype network is an absurd immune system.

paper_content:

Idiotypic networks have attained the status of unavoidable necessities in the regulation of immune responses. In this article Rod Langman and Mel Cohn contend that the conceptual foundations for such idiotypic networks are formal absurdities.
---
paper_title: AIS Based Robot Navigation in a Rescue Scenario

paper_content:

An architecture for a robot control is proposed which is based on the requirements from the RoboCup and AAAI Rescue Robot Competition. An artificial immune system comprises the core component. The suitability of this architecture for the competition and related scenarios, including the modelling of the environment, was verified by simulation.
---
paper_title: Danger Theory: The Link between AIS and IDS?

paper_content:

We present ideas about creating a next generation Intrusion Detection System based on the latest immunological theories. The central challenge with computer security is determining the difference between normal and potentially harmful activity. For half a century, developers have protected their systems by coding rules that identify and block specific events. However, the nature of current and future threats in conjunction with ever larger IT systems urgently requires the development of automated and adaptive defensive tools. A promising solution is emerging in the form of Artificial Immune Systems. The Human Immune System can detect and defend against harmful and previously unseen invaders, so can we not build a similar Intrusion Detection System for our computers.
---
paper_title: Soft Computing-Based Navigation Schemes for a Real Wheeled Robot Moving Among Static Obstacles

paper_content:

Collision-free, time-optimal navigation of a real wheeled robot in the presence of some static obstacles is undertaken in the present study. Two soft computing-based approaches, namely genetic-fuzzy system and genetic-neural system and a conventional potential field approach have been developed for this purpose. Training is given to the soft computing-based navigation schemes off-line and the performance of the optimal motion planner is tested on a real robot. A CCD camera is used to collect information of the environment. After processing the collected data, the communication between the robot and the host computer is obtained with the help of a radio-frequency module. Both the soft computing-based approaches are found to perform better than the potential field method in terms of the traveling time taken by the robot. Moreover, the performance of fuzzy logic-based motion planner is found to be comparable with that of neural network-based motion planner, although the training of the former is seen to be computationally less expensive than the latter. Sometimes the potential field method is unable to yield any feasible solution, specifically when the obstacle is found to be just ahead of the robot, whereas soft computing-based approaches have tackled such a situation well.
---
paper_title: Solving the potential field local minimum problem using internal agent states

paper_content:

We propose a new, extended artificial potential field method, which uses dynamic internal agent states. The internal states are modeled as a dynamical system of coupled first order differential equations that manipulate the potential field in which the agent is situated. The internal state dynamics are forced by the interaction of the agent with the external environment. Local equilibria in the potential field are then manipulated by the internal states and transformed from stable equilibria to unstable equilibria, allowing escape from local minima in the potential field. This new methodology successfully solves reactive path planning problems, such as a complex maze with multiple local minima, which cannot be solved using conventional static potential fields.
---
paper_title: An immunological approach to mobile robot reactive navigation

paper_content:

In this paper, a reactive immune network (RIN) is proposed and employed for mobile robot navigation within unknown environments. Rather than building a detailed mathematical model of artificial immune systems, this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback. In addition, an adaptive virtual target method is integrated to solve the local minima problem in navigation. Several trapping situations designed by the early researchers are adopted to evaluate the performance of the proposed architecture. Simulation results show that the mobile robot is capable of avoiding obstacles, escaping traps, and reaching the goal efficiently and effectively.
---
paper_title: Swarm Robotics: From Sources of Inspiration to Domains of Application

paper_content:

Swarm robotics is a novel approach to the coordination of large numbers of relatively simple robots which takes its inspiration from social insects. This paper proposes a definition to this newly emerg- ing approach by 1) describing the desirable properties of swarm robotic systems, as observed in the system-level functioning of social insects, 2) proposing a definition for the term swarm robotics, and putting for- ward a set of criteria that can be used to distinguish swarm robotics research from other multi-robot studies, 3) providing a review of some studies which can act as sources of inspiration, and a list of promising domains for the utilization of swarm robotic systems.
---
paper_title: Virtual local target method for avoiding local minimum in potential field based robot navigation

paper_content:

A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.
---

Title: Immuno-inspired robotic applications: a review

Section 1: Introduction
Description 1: Summarize the background, motivation, and structure of the paper.

Section 2: AIS Definitions
Description 2: Provide an overview of various AIS models and their relevance to robotic applications.

Section 3: Clonal Selection
Description 3: Describe the clonal selection theory, its computational interpretation, and its general algorithm.

Section 4: Immune Network
Description 4: Discuss the immune network theory, its computational interpretation, and its general algorithm.

Section 5: Danger Theory
Description 5: Explain the danger theory, its computational interpretation, and its general algorithm.

Section 6: AIS-based Robotic Applications
Description 6: Categorize and describe various AIS-based robotic applications and their computational details, including applications using clonal selection, immune network, danger theory, and other approaches.

Section 7: Using AIS
Description 7: Analyze the effectiveness of AIS in robotic applications, covering various findings and their implications.

Section 8: On immuno-inspired robotics
Description 8: Discuss the current trends, challenges, and future directions in immuno-inspired robotics.

Section 9: Conclusions
Description 9: Summarize the key insights, gaps, and potential future research directions in the field of immuno-inspired robotic applications.