Minimum-Violation Temporal Logic Planning for Heterogeneous Robots under Robot Skill Failures

TL;DR

This paper introduces a reactive planning algorithm for heterogeneous robot teams that adapts to skill failures, prioritizes critical tasks, and minimizes mission violations in complex temporal logic tasks.

Contribution

It presents a novel reactive LTL planning method that reassigns tasks and revises plans in real-time to handle robot skill failures, ensuring mission completion or minimal violations.

Findings

Algorithm effectively reassigns tasks during failures.

Theoretical conditions guarantee minimum-violation plans.

Experimental results validate efficiency on simulations and hardware.

Abstract

In this paper, we consider teams of robots with heterogeneous skills (e.g., sensing and manipulation) tasked with collaborative missions described by Linear Temporal Logic (LTL) formulas. These LTL-encoded tasks require robots to apply their skills to specific regions and objects in a temporal and logical order. While existing temporal logic planning algorithms can synthesize correct-by-construction plans, they typically lack reactivity to unexpected failures of robot skills, which can compromise mission performance. This paper addresses this challenge by proposing a reactive LTL planning algorithm that adapts to unexpected failures during deployment. Specifically, the proposed algorithm reassigns sub-tasks to robots based on their functioning skills and locally revises team plans to accommodate these new assignments and ensure mission completion. The main novelty of the proposed algorithm is its ability to handle cases where mission completion becomes impossible due to limited functioning robots. Instead of reporting mission failure, the algorithm strategically prioritizes the most crucial sub-tasks and locally revises the team's plans, as per user-specified priorities, to minimize mission violations. We provide theoretical conditions under which the proposed framework computes the minimum-violation task reassignments and team plans. We provide numerical and hardware experiments to demonstrate the efficiency of the proposed method.