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

Jinwoo Park, Harish Ravichandar, Seth Hutchinson

Keywords: Heterogeneous Multi-Robot Systems, Task Allocation, Scheduling, Cooperating Robots

TL;DR: TRAITS formalizes richer trait models and optimizes coalition trait throughput to allocate heterogeneous robots under battery and temporal constraints for feasible, efficient schedules.

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 durations 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.

Area: Robotics and Control (ROBOT)

Generative A I: I acknowledge that I have read and will follow this policy.

Submission Number: 917