Robot Path Planning using An Ant Colony Optimization Approach: A Survey
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paper_title: Ant Colony System Based Mobile Robot Path Planning

paper_content:

Ant colony optimization (ACO) is a new evolvement algorithm that is proposed by Dorigo M., and solves some task allocation and target search problems to program the motion path searching food. The topic of the article uses the ant colony optimization algorithm to mobile robot system, and solve the problem of mobile robot path planning such that the target point in a collision free space. The simulated results presents that ACO can finds the optimization motion path for mobile robot moving to the target position (food) from the start position (nest) in a collision-free environment.
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paper_title: Robot global path planning overview and a variation of ant colony system algorithm

paper_content:

This paper presents the results of a research that aims to develop an algorithm to solve robot path planning (RPP) problems in static environments. The problem is to find a global optimal path that satisfies the optimization criteria of shortest path with minimum computation time. A description of a variation of Ant Colony System (ACS) algorithm utilized for Robot Path Planning (RPP) purposes is presented. A representation of heuristic and visibility equation of state transition rules is proposed to sustain the function of Ant Colony System (ACS) for solving RPP problem of finding the optimal path. This algorithm was applied within a global static map that consists of feasible free space nodes. The performance of the algorithm in terms of computation time and number of iteration required to obtain an optimal path were evaluated by using a simulation approach. Subsequently, its performance was compared to the performance of Genetic Algorithm (GA) a well known and established RPP algorithm. The results obtained indicate that the developed algorithm performed much better than the GA. In addition, an overview of robot path planning (PP) algorithms in global static environment is also offered.
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paper_title: Finding Optimal Paths on Terrain Maps using Ant Colony Algorithm

paper_content:

This paper presents the meta-heuristic method of ant colony optimization (ACO) to find optimal paths on terrain map images. The procedure simulates decision-making process of ant colonies as they forage for food. Modifications have been made to the ACO algorithm to solve the optimal path finding problem by optimizing multiple constraints. The number of constraints considered here is two. However, it can effectively be used for more than two constraints.
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paper_title: Mobile robot path planning using ant colony optimization

paper_content:

In this paper, the ant colony optimization (ACO) metaheuristic is proposed to solve the mobile robot path planning (MRPP) problem. In order to demonstrate the effectiveness of ACO in solving the MRPP problem, several maps of varying complexity used by an earlier researcher is used for evaluation. Each map consists of static obstacles and walls in different arrangements. Besides that, each map has a grid representation with an equal number of rows and columns. These maps have a starting point and a destination as well. At the beginning of the problem, the ants (representing the mobile robot) are placed at the starting point. The ants would then have to find their way towards the destination whilst avoiding all the obstacles and walls along the way. The ants should also do so with the shortest distance possible. The performance of the proposed ACO metaheuristic is tested on a given set of maps and the results are compared with those reported in the literature. The performance of the proposed ACO metaheuristic is found to be better than the result reported in the literature.
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paper_title: Ant Colony Optimization algorithm for robot path planning

paper_content:

Path planning is an essential task for the navigation and motion control of autonomous robot manipulators. This NP-complete problem is difficult to solve, especially in a dynamic environment where the optimal path needs to be rerouted in real-time when a new obstacle appears. The ACO (Ant Colony Optimization) algorithm is an optimization technique based on swarm intelligence. This paper investigates the application of ACO to robot path planning in a dynamic environment. Two different pheromone re-initialization schemes are compared and computer simulation results are presented.
---
paper_title: Robot global path planning overview and a variation of ant colony system algorithm

paper_content:

This paper presents the results of a research that aims to develop an algorithm to solve robot path planning (RPP) problems in static environments. The problem is to find a global optimal path that satisfies the optimization criteria of shortest path with minimum computation time. A description of a variation of Ant Colony System (ACS) algorithm utilized for Robot Path Planning (RPP) purposes is presented. A representation of heuristic and visibility equation of state transition rules is proposed to sustain the function of Ant Colony System (ACS) for solving RPP problem of finding the optimal path. This algorithm was applied within a global static map that consists of feasible free space nodes. The performance of the algorithm in terms of computation time and number of iteration required to obtain an optimal path were evaluated by using a simulation approach. Subsequently, its performance was compared to the performance of Genetic Algorithm (GA) a well known and established RPP algorithm. The results obtained indicate that the developed algorithm performed much better than the GA. In addition, an overview of robot path planning (PP) algorithms in global static environment is also offered.
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paper_title: An ant colony optimization approach to the probabilistic traveling salesman problem

paper_content:

The Probabilistic Traveling Salesman Problem (PTSP) is a TSP problem where each customer has a given probability of requiring a visit. The goal is to find an a priori tour of minimal expected length over all customers, with the strategy of visiting a random subset of customers in the same order as they appear in the a priori tour.We address the question of whether and in which context an a priori tour found by a TSP heuristic can also be a good solution for the PTSP. We answer this question by testing the relative performance of two ant colony optimization algorithms, Ant Colony System (ACS) introduced by Dorigo and Gambardella for the TSP, and a variant of it (pACS) which aims to minimize the PTSP objective function.We show in which probability configuration of customers pACS and ACS are promising algorithms for the PTSP.
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paper_title: AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks

paper_content:

In this paper, we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. The algorithm is based on the nature-inspired ant colony optimisation framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in the field. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet's performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks. Copyright © 2005 AEIT.
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paper_title: New ideas for applying ant colony optimization to the probabilistic TSP

paper_content:

The Probabilistic Traveling Salesperson Problem (PTSP) is a stochastic variant of the Traveling Salesperson Problem (TSP); each customer has to be serviced only with a given probability. The goal is to find an a priori tour with shortest expected tour-length, with the customers being served in the specified order and customers not requiring service being skipped. In this paper, we use the Ant Colony Optimization (ACO) metaheuristic to construct solutions for PTSP. We propose two new heuristic guidance schemes for this problem, and examine the idea of using approximations to calculate the expected tour length. This allows to find better solutions or use less time than the standard ACO approach.
---
paper_title: Ant Colony Optimization algorithm for robot path planning

paper_content:

Path planning is an essential task for the navigation and motion control of autonomous robot manipulators. This NP-complete problem is difficult to solve, especially in a dynamic environment where the optimal path needs to be rerouted in real-time when a new obstacle appears. The ACO (Ant Colony Optimization) algorithm is an optimization technique based on swarm intelligence. This paper investigates the application of ACO to robot path planning in a dynamic environment. Two different pheromone re-initialization schemes are compared and computer simulation results are presented.
---
paper_title: MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows

paper_content:

MACS-VRPTW, an Ant Colony Optimization based approach useful to solve vehicle routing problems with time windows is presented. MACS-VRPTW is organized with a hierarchy of artificial ant colonies designed to successively optimize a multiple objective function: the first colony minimizes the number of vehicles while the second colony minimizes the traveled distances. Cooperation between colonies is performed by exchanging information through pheromone updating. We show that MACS-VRPTW is competitive with the best known existing methods both in terms of solution quality and computation time. Moreover, MACS-VRPTW improves some of the best solutions known for a number of problem instances in the literature.
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paper_title: ACO / F-Race : Ant Colony Optimization and Racing Techniques for Combinatorial Optimization Under Uncertainty

paper_content:

The paper introduces ACO/F-Race, an algorithm for tackling combinatorial optimization problems under uncertainty. The algorithm is based on ant colony optimization and on F-Race. The latter is a general method for the comparison of a number of candidates and for the selection of the best one according to a given criterion. Some experimental results on the probabilistic traveling salesman problem are presented.
---
paper_title: Ant Colony Optimization algorithm for robot path planning

paper_content:

Path planning is an essential task for the navigation and motion control of autonomous robot manipulators. This NP-complete problem is difficult to solve, especially in a dynamic environment where the optimal path needs to be rerouted in real-time when a new obstacle appears. The ACO (Ant Colony Optimization) algorithm is an optimization technique based on swarm intelligence. This paper investigates the application of ACO to robot path planning in a dynamic environment. Two different pheromone re-initialization schemes are compared and computer simulation results are presented.
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paper_title: AntNet: Distributed Stigmergetic Control for Communications Networks

paper_content:

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.
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paper_title: AntNet: Distributed Stigmergetic Control for Communications Networks

paper_content:

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.
---
paper_title: AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks

paper_content:

In this paper, we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. The algorithm is based on the nature-inspired ant colony optimisation framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in the field. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet's performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks. Copyright © 2005 AEIT.
---
paper_title: An ant colony optimization approach to the probabilistic traveling salesman problem

paper_content:

The Probabilistic Traveling Salesman Problem (PTSP) is a TSP problem where each customer has a given probability of requiring a visit. The goal is to find an a priori tour of minimal expected length over all customers, with the strategy of visiting a random subset of customers in the same order as they appear in the a priori tour.We address the question of whether and in which context an a priori tour found by a TSP heuristic can also be a good solution for the PTSP. We answer this question by testing the relative performance of two ant colony optimization algorithms, Ant Colony System (ACS) introduced by Dorigo and Gambardella for the TSP, and a variant of it (pACS) which aims to minimize the PTSP objective function.We show in which probability configuration of customers pACS and ACS are promising algorithms for the PTSP.
---
paper_title: A converging ACO algorithm for stochastic combinatorial optimization

paper_content:

The paper presents a general-purpose algorithm for solving stochastic combinatorial optimization problems with the expected value of a random variable as objective and deterministic constraints. The algorithm follows the Ant Colony Optimization (ACO) approach and uses Monte-Carlo sampling for estimating the objective. It is shown that on rather mild conditions, including that of linear increment of the sample size, the algorithm converges with probability one to the globally optimal solution of the stochastic combinatorial optimization problem. Contrary to most convergence results for metaheuristics in the deterministic case, the algorithm can usually be recommended for practical application in an unchanged form, i.e., with the ”theoretical” parameter schedule.
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paper_title: ACO / F-Race : Ant Colony Optimization and Racing Techniques for Combinatorial Optimization Under Uncertainty

paper_content:

The paper introduces ACO/F-Race, an algorithm for tackling combinatorial optimization problems under uncertainty. The algorithm is based on ant colony optimization and on F-Race. The latter is a general method for the comparison of a number of candidates and for the selection of the best one according to a given criterion. Some experimental results on the probabilistic traveling salesman problem are presented.
---
paper_title: New ideas for applying ant colony optimization to the probabilistic TSP

paper_content:

The Probabilistic Traveling Salesperson Problem (PTSP) is a stochastic variant of the Traveling Salesperson Problem (TSP); each customer has to be serviced only with a given probability. The goal is to find an a priori tour with shortest expected tour-length, with the customers being served in the specified order and customers not requiring service being skipped. In this paper, we use the Ant Colony Optimization (ACO) metaheuristic to construct solutions for PTSP. We propose two new heuristic guidance schemes for this problem, and examine the idea of using approximations to calculate the expected tour length. This allows to find better solutions or use less time than the standard ACO approach.
---
paper_title: MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows

paper_content:

MACS-VRPTW, an Ant Colony Optimization based approach useful to solve vehicle routing problems with time windows is presented. MACS-VRPTW is organized with a hierarchy of artificial ant colonies designed to successively optimize a multiple objective function: the first colony minimizes the number of vehicles while the second colony minimizes the traveled distances. Cooperation between colonies is performed by exchanging information through pheromone updating. We show that MACS-VRPTW is competitive with the best known existing methods both in terms of solution quality and computation time. Moreover, MACS-VRPTW improves some of the best solutions known for a number of problem instances in the literature.
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paper_title: Ant colony optimization for continuous domains

paper_content:

In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We present the general idea, implementation, and results obtained. We compare the results with those reported in the literature for other continuous optimization methods: other ant-related approaches and other metaheuristics initially developed for combinatorial optimization and later adapted to handle the continuous case. We discuss how our extended ACO compares to those algorithms, and we present some analysis of its efficiency and robustness.
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paper_title: ACO for continuous and mixed-variable optimization

paper_content:

This paper presents how the Ant Colony Optimization (ACO) metaheuristic can be extended to continuous search domains and applied to both continuous and mixed discrete-continuous optimization problems. The paper describes the general underlying idea, enumerates some possible design choices, presents a first implementation, and provides some preliminary results obtained on well-known benchmark problems. The proposed method is compared to other ant, as well as non-ant methods for continuous optimization.
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Title: Robot Path Planning using An Ant Colony Optimization Approach: A Survey

Section 1: INTRODUCTION
Description 1: Provide an overview of the robot path planning problem, including its challenges, requirements, and classification based on the environment and awareness.

Section 2: REVIEW OF LITERATURE
Description 2: Summarize various studies and algorithms proposed by researchers for robot path planning using the Ant Colony Optimization approach.

Section 3: PATH PLANNING TECHNIQUES
Description 3: Describe different techniques for path planning, including Particle Swarm Optimization, Genetic Algorithm, Tabu Search, Simulated Annealing, Reactive Search Optimization, and Ant Colony Algorithms.

Section 4: ALGORITHMS FOR ROBOT PATH PLANNING
Description 4: Detail specific algorithms implemented for robot path planning, including their steps, equations, and mechanisms to handle obstacles.

Section 5: COMPARISON BETWEEN ALGORITHMS
Description 5: Compare different proposed algorithms based on their variations and performance in overcoming the Ant Colony algorithm's drawbacks.

Section 6: APPLICATIONS OF ANT COLONY ALGORITHM
Description 6: Discuss the applications of the Ant Colony Optimization algorithm in various fields, such as NP-Hard problems, telecommunication networks, and industrial problems.

Section 7: CURRENT RESEARCH TOPICS IN ACO
Description 7: Outline ongoing research topics in Ant Colony Optimization, including dynamic optimization problems, stochastic optimization problems, multi-objective optimization, and continuous optimization.

Section 8: CONCLUSION
Description 8: Conclude the paper by summarizing the findings, discussing the limitations of current algorithms, and suggesting future research directions.