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.

翻译：协作自主系统（如机器人集群）的集体性能或能力，共同受到该集体中个体系统的形态与行为的影响。在此背景下，本文探讨了形态如何影响执行侦察与搜救任务的无人空中/地面机器人所习得的战术行为。为实现此目标，本文提出了一个计算高效的框架，以解决原本具有挑战性的集群机器人形态与战术行为联合优化问题。为此，关键的新颖进展包括：引入物理天赋度量指标，以及对图强化学习架构进行修改，从而允许联合学习集群战术策略与约束执行这些战术的空中机器人机动性和目标/受害者搜索能力的天赋度量指标（搜索速度、飞行航程和巡航速度）。该协同设计方法的实施得益于一个基于开源Pybullet的集群模拟器的改进，该模拟器支持使用可变的空中资产能力。通过任务性能指标的比较，观察发现协同设计的结果优于采用固定帕累托设计的战术学习结果。此外，通过比较基线设计与协同设计的结果，也观察到了形态与习得行为上的显著差异。