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

• URL: http://arxiv.org/abs/2602.13866v1
• Date: Sat, 14 Feb 2026 19:55:15 GMT
• Title: Modeling and Optimizing the Provisioning of Exhaustible Capabilities for Simultaneous Task Allocation and Scheduling
• Abstract summary: We present a comprehensive, time-extended, offline heterogeneous multi-robot task allocation framework, TRAITS.<n>We introduce a nonlinear programming-based trait distribution module that can optimize the trait-provisioning rate of coalitions.<n>We evaluate TRAITS against two state-of-the-art frameworks, with results demonstrating its advantage in satisfying complex trait and battery requirements.
• Score: 11.537906063717974
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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