1 research outputs found
Trust-based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop
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 this paper, distributed symbolic motion
planning for multi-robot systems is developed to address scalability. More
specifically, compositional reasoning approaches are developed to decompose the
global planning problem, and atomic propositions for observation,
communication, and control are proposed to address inter-robot collision
avoidance. To improve solution quality and adaptability, a dynamic,
quantitative, and probabilistic human-to-robot trust model is developed to aid
this decomposition. Furthermore, a trust-based real-time switching framework is
proposed to switch between autonomous and manual motion planning for tradeoffs
between task safety and efficiency. Deadlock- and livelock-free algorithms are
designed to guarantee reachability of goals with a human-in-the-loop. A set of
non-trivial multi-robot simulations with direct human input and trust
evaluation are provided demonstrating the successful implementation of the
trust-based multi-robot symbolic motion planning methods.Comment: 33 pages, 21 figure