Trust-based human-robot interaction for multi-robot symbolic motion planning
Trust-based human-robot interaction for multi-robot symbolic motion planning
复制标题
基于信任的人机交互，用于多机器人符号运动规划
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Laura R. Humphrey
中科院分区：
文献类型：
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作者：
David A. Spencer;Yue Wang;Laura R. Humphrey
机器人符号运动规划是在离散空间中指定和规划机器人任务，然后以保持离散级任务规范的方式在连续空间中执行这些任务的过程。尽管在符号运动规划方面取得了进展，但仍然存在许多挑战，包括解决多机器人系统的可扩展性问题，以及通过以自适应方式结合人类智能来改进解决方案。在本文中，我们使用本地通信，观察，控制协议和组合推理的方法来分解规划问题，以解决可扩展性。为了解决解决方案的质量和适应性，我们使用了一个动态和计算的信任模型，以帮助这种分解，并实现自动化和人类运动规划之间的实时切换。提供了一个模拟演示这些方法的成功实施。
Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence in an adaptive fashion. In this paper, we use local communication, observation, control protocols, and compositional reasoning approaches to decompose the planning problem to address scalability. To address solution quality and adaptability, we use a dynamic and computational trust model to aid this decomposition and to implement real-time switching between automated and human motion planning. A simulation is provided demonstrating the successful implementation of these methods.