Name
Abstract | ||
|---|---|---|
In this paper, we consider the target searching problems with a new type of the object: decoys which can be sensed exactly as targets but cannot be collected by the robots. In real-life applications, decoys are very common especially for swarm robots whose hardware should be designed as simple and cheap as possible. This inevitably brings errors and mistakes in the sensing results and the swarm may mistakenly sense certain kinds of environment objects as the target they are looking for. We proposed a simple cooperative strategy to solve this problem, comparing with a non-cooperative strategy as the baseline. The strategies work with other searching algorithms and provide schemes for avoiding decoys. Simulation results demonstrate that the cooperative strategy shares almost the same computation overload yet has better performance in iterations and especially visited times of decoys. The strategy shows great adaptiveness to large scale problems and performs better when more decoys or robots exist in the simulation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CEC.2014.6900376 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
environment objects,swarm robotics,decoy avoidance,multi-robot systems,multiple target searching problems,search problems,noncooperative strategy,cooperative strategy | Search algorithm,Swarm behaviour,Computer science,Cooperative strategy,Artificial intelligence,Robot,Machine learning,Swarm robotics,Computation | Conference |
Citations | PageRank | References |
3 | 0.38 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhongyang Zheng | 1 | 25 | 2.94 |
| Junzhi Li | 2 | 132 | 7.72 |
| Jie Li | 3 | 9 | 2.85 |
| Ying Tan | 4 | 1286 | 95.40 |