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.
['David A. Spencer', 'Yue Wang', 'Laura Humphrey']
Trust-based human-robot interaction for multi-robot symbolic motion planning