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

TL;DR

This paper presents a comprehensive, time-extended, offline heterogeneous multi-robot task allocation framework, TRAITS, which it is believed to be the first that can cope with the provisioning of exhaustible traits under battery and temporal constraints.

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.

Figures (5)

• Figure 1: A motivating scenario: An autonomous warehouse with heterogeneous robots (top), characterized by traits that have provisioning and exhaustibility attributes (bottom), collaborating on complex tasks subject to temporal and battery constraints.
• Figure 2: The proposed model captures various trait aspects and the framework handles all categories in the diagram.
• Figure 3: High-level architecture of the *traits framework.
• Figure 4: Fraction of under-resourced robots (robots assigned to tasks without sufficient traits). Lower values (green) indicate better performance. *traits consistently avoids assigning robots to tasks that exceed their trait capacities, whereas *itags and *ctas tend to assign insufficiently resourced robots as the task load grows for a fixed team size. This behavior partly stems from the assumption of irreducible traits in *itags and *ctas. Under-resourcing declines with increasing team size.
• Figure 5: Computation time as a function of the number of tasks $K$ (left) and the number of robots $N$ (right). Solid dots indicate the mean, and the shaded regions represent the standard error computed from 120 experiments. The results highlight that computation time increases with both parameters, with task count having a more pronounced impact.