面向具备物理能力的空中机器人及其战术智能集群行为：一种高效的协同设计方法
Towards Physically Talented Aerial Robots with Tactically Smart Swarm Behavior thereof: An Efficient Co-design Approach
AI总结：
本研究提出计算高效的协同设计框架，结合物理能力指标与改进的图强化学习架构，联合优化空中集群机器人的形态与战术行为，实验表明其任务性能优于固定帕累托设计方案。
摘要：
协作自主系统（如机器人群）的集体性能或能力，受集群中个体系统的形态与行为的共同影响。在此背景下，本文探究了形态对执行侦察及搜索救援任务的无人空中/地面机器人所学战术行为的影响。为此，本文提出了一个计算高效的框架，以解决集群机器人形态与战术行为联合优化这一原本颇具挑战性的问题。\n该研究的关键创新点包括：引入物理能力指标，以及对图强化学习架构进行改进，以实现集群战术策略与能力指标（搜索速度、飞行航程和巡航速度）的联合学习，这些指标会约束执行战术的空中机器人的机动性以及目标/受害者搜索能力。\n该协同设计方法的实现，得益于一款基于Pybullet的开源集群模拟器的改进，该模拟器支持使用可变的空中资产能力。实验结果显示，在任务性能指标方面，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。通过对比基线设计与协同设计的结果，还可观察到二者在形态和所学行为上存在显著差异。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
