论文标题
机器人内部注意建模，用于异质多机器人的任务自适应组合
Robot Inner Attention Modeling for Task-Adaptive Teaming of Heterogeneous Multi Robots
论文作者
论文摘要
由团队尺度和功能多样性吸引，异质的多机器人系统（HMR），其中多个具有不同功能和数字的机器人是协调执行任务的多个机器人，已被广泛用于复杂和大规模的场景，包括灾难搜索和救援，现场监视，现场监视和社会保障。但是，由于任务要求的多样性，精确地组成具有适当尺寸和功能的机器人团队以动态满足任务需求，同时将机器人资源成本限制在低水平上是一个挑战。为了解决这个问题，在本文中，开发了一种新颖的自适应合作方法，即内在注意力（Inneratt），以灵活地团队异质机器人，以随着任务类型和环境的变化而执行任务。 Inneratt的设计是通过将新颖的注意机制纳入多代理参与者的批判性增强架构。通过注意机制，将分析机器人能力以灵活地组成团队以满足任务要求。设计了具有不同任务品种的方案（“单个任务”，“双任务”和“混合任务”）。 Inneratt的有效性是通过其在灵活合作中的准确性来验证的。
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.
