Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints

TL;DR

This paper introduces SCoBA, a hierarchical algorithm for dynamic multi-robot task allocation under uncertainty and time constraints, achieving high success rates and scalability in simulations.

Contribution

The paper presents SCoBA, a novel hierarchical algorithm that efficiently solves stochastic multi-robot task allocation problems with temporal constraints, outperforming baseline methods.

Findings

SCoBA achieves higher successful task completion rates than baselines.

The algorithm scales well with increasing tasks and agents.

SCoBA performs competitively against an oracle with complete lookahead.

Abstract

We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of unsuccessful tasks at the end of the operation horizon. We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination and addresses them in a hierarchical manner. The lower layer computes policies for individual agents using dynamic programming with tree search, and the upper layer resolves conflicts in individual plans to obtain a valid multi-agent allocation. Our algorithm, Stochastic Conflict-Based Allocation (SCoBA), is optimal in expectation and complete under some reasonable assumptions. In practice, SCoBA is computationally efficient enough to interleave planning and execution online. On the metric of successful task completion, SCoBA consistently outperforms a number of baseline methods and shows strong competitive performance against an oracle with complete lookahead. It also scales well with the number of tasks and agents. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.