Open Access
Anytime Guaranteed Search using Spanning Trees
Geoffrey A. Hollinger,Athanasios Kehagias,Sanjiv Singh,Dave Ferguson,Siddhartha S. Srinivasa +4 more
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TLDR
This technical report presents an anytime algorithm for solving the multi-robot guaranteed search problem and takes advantage of the temporal characteristics of the search schedule to reuse guards no longer necessary at their previous locations.Abstract:
This technical report presents an anytime algorithm for solving the multi-robot guaranteed search problem. Guaranteed search requires a team of robots to clear an environment of a potentially adversarial target. In other words, a team of searchers must generate a search strategy guaranteed to find a target. This problem is known to be NP-complete on arbitrary graphs but can be solved in linear-time on trees. Our proposed algorithm reduces an environment to a graph representation and then randomly generates a spanning tree of the graph. Guards are then placed on nodes in the graph to eliminate non-tree edges, and a search strategy for the remaining tree is found using a linear-time algorithm from prior work. To reduce the number of guards, our algorithm takes advantage of the temporal characteristics of the search schedule to reuse guards no longer necessary at their previous locations. Many spanning trees are analyzed prior to search to determine the tree that yields the best search strategy. At any time, spanning tree generation can be stopped yielding the best strategy thus far. Our proposed algorithm is demonstrated on two complex graphs derived from physical environments, and several methods for generating candidate spanning trees are compared.read more
Citations
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Journal ArticleDOI
A graph search algorithm for indoor pursuit/evasion
TL;DR: The IGNS (Iterative Greedy Node Search) algorithm is presented, which performs offline guaranteed search (i.e. no matter how the evader moves, it will eventually be captured) and produces an internal search.
Journal ArticleDOI
Improving the Efficiency of Clearing with Multi-agent Teams
TL;DR: This work proposes treating search as a resource allocation problem, which leads to a scalable anytime algorithm for generating schedules that clear the environment of a worst-case adversarial target and have good average-case performance considering a non-adversarial motion model.
Proceedings ArticleDOI
Pursuit-evasion in 2.5d based on team-visibility
TL;DR: The presented approach is the first viable solution to 2.5d pursuit-evasion with height maps and constructs a graph representation of the environment by sampling strategic locations and computing their detection sets, an extended notion of visibility.
Proceedings ArticleDOI
Efficient, guaranteed search with multi-agent teams
TL;DR: An algorithm that combines finite-horizon planning with spanning tree traversal methods to generate plans that clear the environment of a worst-case adversarial target and have good average-case performance considering a target motion model is introduced.
References
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MonographDOI
Planning Algorithms: Introductory Material
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Book
Treewidth: Computations and Approximations
TL;DR: Testing superperfection of k-trees and triangulating 3-colored graphs results in approximating treewidth and pathwidth for some classes of perfect graphs.
Proceedings ArticleDOI
Generating random spanning trees more quickly than the cover time
TL;DR: This paper gives a new algorithm for generating random spanning trees of an undirected graph that is easy to code up, has small running time constants, and has a nice proof that it generates trees with the right probabilities.