Trust-based Symbolic Motion Planning for Multi-robot Bounding Overwatch

TL;DR

This paper introduces a trust-based decentralized symbolic motion planning framework for multi-robot bounding overwatch, ensuring safety, correctness, and reliability through trust modeling and optimal plan selection, validated via ROS Gazebo simulations.

Contribution

It develops a novel decentralized SMP framework incorporating a computational trust model to enhance multi-robot coordination for complex tasks.

Findings

Trust model accurately predicts robot behavior in terrain.

Decentralized planning guarantees correctness and parallel execution.

Simulation results demonstrate improved reliability and safety.

Abstract

Multi-robot bounding overwatch requires timely coordination of robot team members. Symbolic motion planning (SMP) can provide provably correct solutions for robot motion planning with high-level temporal logic task requirements. This paper aims to develop a framework for safe and reliable SMP of multi-robot systems (MRS) to satisfy complex bounding overwatch tasks constrained by temporal logics. A decentralized SMP framework is first presented, which guarantees both correctness and parallel execution of the complex bounding overwatch tasks by the MRS. A computational trust model is then constructed by referring to the traversability and line of sight of robots in the terrain. The trust model predicts the trustworthiness of each robot team's potential behavior in executing a task plan. The most trustworthy task and motion plan is explored with a Dijkstra searching strategy to guarantee the reliability of MRS bounding overwatch. A robot simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the proposed framework.