Date of Award


Embargo Period


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Robotics Institute


Sanjiv Singh

Second Advisor

Geoffrey Gordon

Third Advisor

Reid Simmons

Fourth Advisor

Athanasios Kehagias


This thesis examines search in the physical world, which differs significantly from
the searches in the digital world that we perform every day on our computers. When
searching the internet, for instance, success is a matter of informed indexing that
allows the information to be retrieved quickly. In these cases, there is no consideration
of the physical nature of the world, and the search is not cognizant of space,
time, or traversal distance. In contrast, search in the physical world must consider a
target that could be continuously moving, possibly even trying to evade being found.
The environment may be partially known, and the search proceeds with information
gathered during the search itself. In many cases, such as guaranteeing capture of
an adversarial target, the problem cannot be solved with a single searcher, and all
group members must coordinate their actions with others on the team. Prior work
has explored limited instances of such problems, but existing techniques either scale
poorly or do not have performance guarantees.
Two of the main variations of search in the physical world are considered: efficient
search and guaranteed search. During efficient search, robots move to optimize the
average-case performance of the search given a model of the target’s motion. During
guaranteed search, robots coordinate to minimize the worst-case search time if
the target is adversarial. This thesis unifies these search problems and shows them
to be NP-hard, which suggests that a scalable and optimal algorithm is unlikely.
In addition, it is shown that efficient search admits a bounded approximation, and
guaranteed search does not. Despite these hardness results, algorithms using implicit
coordination can provide scalable and high-performing solutions to many real-world
search problems. Implicit coordination is defined as the sharing of locations, measurements,
and/or actions to improve the team plan. In accord with this design strategy,
a linearly scalable efficient search algorithm is presented that utilizes implicit coordination
to achieve bounded performance. In addition, this thesis contributes a novel
approach that augments the coordination with a pre-search spanning tree generation
step, which leads to an anytime algorithm for guaranteed search.
With a focus on decentralized and online operation, the proposed search algorithms
are extended to take into account team constraints, limited communication,
and partially known environments. The techniques are illustrated using a scenario
from the literature that incorporates both efficient and guaranteed search, and they
are validated both in simulation and on human-robot search teams operating in the
physical world. The developed framework enables teams of autonomous agents to
search environments outside the scope of previous techniques, and the analysis provides
insight into the complexity of multi-robot coordination problems