物理的に優れた空中ロボットとその戦術的に賢い群れ行動に向けて：効率的な共同設計アプローチ
Towards Physically Talented Aerial Robots with Tactically Smart Swarm Behavior thereof: An Efficient Co-design Approach
群れロボットの形態と戦術行動を同時最適化する計算効率の良いフレームワークを提案し、物理的才能指標とグラフ強化学習を組み合わせて、偵察・捜索救助ミッションでの性能向上を実現した。
著者: Prajit KrisshnaKumar, Steve Paul, Hemanth Manjunatha, Mary Corra, Ehsan Esfahani, Souma Chowdhury
分類: cs.RO, cs.MA
原文アブストラクト
The collective performance or capacity of collaborative autonomous systems such as a swarm of robots is jointly influenced by the morphology and the behavior of individual systems in that collective. In that context, this paper explores how morphology impacts the learned tactical behavior of unmanned aerial/ground robots performing reconnaissance and search & rescue. This is achieved by presenting a computationally efficient framework to solve this otherwise challenging problem of jointly optimizing the morphology and tactical behavior of swarm robots. Key novel developments to this end include the use of physical talent metrics and modification of graph reinforcement learning architectures to allow joint learning of the swarm tactical policy and the talent metrics (search speed, flight range, and cruising speed) that constrain mobility and object/victim search capabilities of the aerial robots executing these tactics. Implementation of this co-design approach is supported by advancements to an open-source Pybullet-based swarm simulator that allows the use of variable aerial asset capabilities. The results of the co-design are observed to outperform those of tactics learning with a fixed Pareto design, when compared in terms of mission performance metrics. Significant differences in morphology and learned behavior are also observed by comparing the baseline design and the co-design outcomes.
