Automated Task Updates of Temporal Logic Specifications for Heterogeneous Robots

TL;DR

This paper presents a framework for automatically updating task assignments in heterogeneous robot teams executing complex temporal logic tasks, ensuring scalability and near-optimal performance.

Contribution

It introduces a heuristic, token-based conflict resolution algorithm for automatic task updates in heterogeneous robot groups with proven scalability.

Findings

The algorithm effectively updates tasks in multi-robot systems.

Simulations demonstrate scalability and near-optimality.

Framework accommodates complex temporal logic specifications.

Abstract

Given a heterogeneous group of robots executing a complex task represented in Linear Temporal Logic, and a new set of tasks for the group, we define the task update problem and propose a framework for automatically updating individual robot tasks given their respective existing tasks and capabilities. Our heuristic, token-based, conflict resolution task allocation algorithm generates a near-optimal assignment for the new task. We demonstrate the scalability of our approach through simulations of multi-robot tasks.