Modeling and Optimizing the Provisioning of Exhaustible Capabilities for Simultaneous Task Allocation and Scheduling

TL;DR

This paper introduces TRAITS, a comprehensive multi-robot task allocation framework that optimizes trait provisioning and battery use over time, improving feasibility and efficiency in complex, resource-constrained scenarios.

Contribution

The paper presents the first offline multi-robot task allocation framework that handles exhaustible traits with nonlinear programming for trait distribution and optimization.

Findings

TRAITS outperforms existing frameworks in satisfying complex trait and battery constraints.

TRAITS provides more accurate feasibility assessments and task time estimations.

The framework remains computationally tractable for large-scale problems.

Abstract

Deploying heterogeneous robot teams to accomplish multiple tasks over extended time horizons presents significant computational challenges for task allocation and planning. In this paper, we present a comprehensive, time-extended, offline heterogeneous multi-robot task allocation framework, TRAITS, which we believe to be the first that can cope with the provisioning of exhaustible traits under battery and temporal constraints. Specifically, we introduce a nonlinear programming-based trait distribution module that can optimize the trait-provisioning rate of coalitions to yield feasible and time-efficient solutions. TRAITS provides a more accurate feasibility assessment and estimation of task execution times and makespan by leveraging trait-provisioning rates while optimizing battery consumption -- an advantage that state-of-the-art frameworks lack. We evaluate TRAITS against two state-of-the-art frameworks, with results demonstrating its advantage in satisfying complex trait and battery requirements while remaining computationally tractable.