Trust-based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

TL;DR

This paper introduces a scalable, trust-based multi-robot symbolic motion planning framework that incorporates human input to improve safety, efficiency, and adaptability in complex multi-robot tasks.

Contribution

It develops a distributed, compositional planning approach with a dynamic trust model and real-time switching between autonomous and manual control.

Findings

Successful multi-robot simulations demonstrate improved safety and efficiency.

Trust-based switching enhances adaptability in dynamic environments.

Algorithms guarantee goal reachability without deadlock or livelock.

Abstract

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.