Attracted by team scale and function diversity, a heterogeneous multi-robot system (HMRS), where multiple robots with different functions and numbers are coordinated to perform tasks, has been widely used for complex and large-scale scenarios, including disaster search and rescue, site surveillance, and social security. However, due to the variety of the task requirements, it is challenging to accurately compose a robot team with appropriate sizes and functions to dynamically satisfy task needs while limiting the robot resource cost to a low level. To solve this problem, in this paper, a novel adaptive cooperation method, inner attention (innerATT), is developed to flexibly team heterogeneous robots to execute tasks as task types and environment change. innerATT is designed by integrating a novel attention mechanism into a multi-agent actor-critic reinforcement learning architecture. With an attention mechanism, robot capability will be analyzed to flexibly form teams to meet task requirements. Scenarios with different task variety ("Single Task", "Double Task", and "Mixed Task") were designed. The effectiveness of the innerATT was validated by its accuracy in flexible cooperation.
翻译:在团队规模和功能多样性的吸引下,一个多式多机器人系统(HMRS)被广泛用于复杂和大规模的情况,包括灾害搜索和救援、现场监视和社会保障等复杂和大规模的情况,但由于任务要求多种多样,很难准确地组成一个具有适当规模和功能的机器人团队,以动态地满足任务需求,同时将机器人资源成本限制在低水平。为了解决这个问题,本文件开发了一种新的适应性合作方法,即内部关注(INERATT),以便灵活地组合具有不同功能的机器人,以执行任务类型和环境变化等任务。Enneratt的设计是将新的关注机制纳入多代理人-行为者强化学习结构。借助一种关注机制,机器人能力将分析为灵活组建团队以满足任务要求。设计了不同任务类型(“单项任务”、“组合任务”和“混合任务”)的情景。核心任务的有效性通过灵活合作的准确性得到了验证。