Reactive Task Allocation and Planning for Quadrupedal and Wheeled Robot Teaming

TL;DR

This paper introduces a reactive, hierarchical multi-robot task allocation and planning framework that handles disturbances in real-time, ensuring task completion for heterogeneous quadrupedal and wheeled robots in complex environments.

Contribution

It presents a novel online reallocation strategy that updates robot plans without full resynthesis, integrating disturbance monitoring with Behavior Trees for robust task execution.

Findings

Effective online reallocation maintains task completion despite disturbances.

Simulation results demonstrate robustness in hospital delivery scenarios.

Framework successfully integrates high-level planning with low-level disturbance recovery.

Abstract

This paper takes the first step towards a reactive, hierarchical multi-robot task allocation and planning framework given a global Linear Temporal Logic specification. The capabilities of both quadrupedal and wheeled robots are leveraged via a heterogeneous team to accomplish a variety of navigation and delivery tasks. However, when deployed in the real world, all robots can be susceptible to different types of disturbances, including but not limited to locomotion failures, human interventions, and obstructions from the environment. To address these disturbances, we propose task-level local and global reallocation strategies to efficiently generate updated action-state sequences online while guaranteeing the completion of the original task. These task reallocation approaches eliminate reconstructing the entire plan or resynthesizing a new task. To integrate the task planner with low-level inputs, a Behavior Tree execution layer monitors different types of disturbances and employs the reallocation methods to make corresponding recovery strategies. To evaluate this planning framework, dynamic simulations are conducted in a realistic hospital environment with a heterogeneous robot team consisting of quadrupeds and wheeled robots for delivery tasks.