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

2026-02-14

AI Summary

This work addresses the challenge of task allocation and scheduling for heterogeneous multi-robot systems in long-duration missions, where depletable resources such as battery capacity impose critical constraints. The paper introduces TRAITS, a novel framework that explicitly models the supply rate of depletable capabilities and integrates it directly into the task assignment process. By formulating a nonlinear program that jointly optimizes coalition composition and capability provisioning strategies, TRAITS generates efficient and feasible schedules that satisfy both temporal and energy constraints. In contrast to existing approaches, TRAITS maintains computational tractability while simultaneously accounting for complex capability requirements and resource consumption dynamics, thereby significantly improving both the feasibility and performance of mission execution.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• task allocation
• exhaustible capabilities
• multi-robot systems
• battery constraints
• scheduling

Innovation

• trait provisioning
• exhaustible capabilities
• multi-robot task allocation
• nonlinear programming
• battery-aware scheduling