A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

TL;DR

This paper introduces an energy-aware, resilient task allocation framework for heterogeneous multi-robot systems, optimizing energy use and robustness in long-term autonomous operations through a novel capability representation and online optimization.

Contribution

It presents a new framework that models robot capabilities and features separately, enhancing resilience and energy efficiency in task allocation for heterogeneous robot teams.

Findings

Framework reduces energy consumption in simulations and real experiments.

System maintains functionality despite environmental changes and robot failures.

Demonstrates improved survivability and efficiency in long-duration tasks.

Abstract

In the context of heterogeneous multi-robot teams deployed for executing multiple tasks, this paper develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Towards this end, the task prioritization and execution -- through which the allocation of tasks to robots is effectively realized -- are encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots, and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.