不確実性下における多エージェント追跡逃避問題の追跡者割当と制御戦略
Pursuer Assignment and Control Strategies in Multi-agent Pursuit-Evasion Under Uncertainties
ノイズを含むセンサ情報からマルコフ局所化で相手位置を確率的に推定し、冗長性を考慮した改良ハンガリアン法で追跡者を割り当てて捕獲時間を最小化する追跡逃避戦略を提案した論文。
著者: Leiming Zhang, Amanda Prorok, Subhrajit Bhattacharya
分類: cs.RO
原文アブストラクト
We consider a pursuit-evasion problem with a heterogeneous team of multiple pursuers and multiple evaders. Although both the pursuers (robots) and the evaders are aware of each others' control and assignment strategies, they do not have exact information about the other type of agents' location or action. Using only noisy on-board sensors the pursuers (or evaders) make probabilistic estimation of positions of the evaders (or pursuers). Each type of agent use Markov localization to update the probability distribution of the other type. A search-based control strategy is developed for the pursuers that intrinsically takes the probability distribution of the evaders into account. Pursuers are assigned using an assignment algorithm that takes redundancy (i.e., an excess in the number of pursuers than the number of evaders) into account, such that the total or maximum estimated time to capture the evaders is minimized. In this respect we assume the pursuers to have clear advantage over the evaders. However, the objective of this work is to use assignment strategies that minimize the capture time. This assignment strategy is based on a modified Hungarian algorithm as well as a novel algorithm for determining assignment of redundant pursuers. The evaders, in order to effectively avoid the pursuers, predict the assignment based on their probabilistic knowledge of the pursuers and use a control strategy to actively move away from those pursues. Our experimental evaluation shows that the redundant assignment algorithm performs better than an alternative nearest-neighbor based assignment algorithm.
