Name
Abstract | ||
|---|---|---|
We present a novel approach for solving pursuit-evasion problems where multiple pursuers with limited sensing capabilities are used to detect all possible mobile evaders in a given environment. We make no assumptions about the number, the speed, or the maneuverability of evaders. Our algorithm takes as input a map of the environment and sensor models for the pursuers. We then obtain a graph representation of an environment using a C. ech Complex. Even with such a representation, the configuration space grows exponentially with the number of pursuers. In order to address this challenge, we propose an abstraction framework to partition the configuration space into sets of topologically similar configurations that preserve the space of possible evader locations. We validate our approach on several simulated environments with varying topologies and numbers of pursuers. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-672-9-1370 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Computer science,Pursuit-evasion,Artificial intelligence,Machine learning | Conference | 285 |
ISSN | Citations | PageRank |
0922-6389 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rattanachai Ramaithitima | 1 | 16 | 1.69 |
| Siddharth Srivastava | 2 | 210 | 25.98 |
| Subhrajit Bhattacharya | 3 | 462 | 36.93 |
| Alberto Speranzon | 4 | 332 | 30.26 |
| Vijay Kumar | 5 | 91 | 31.21 |