Parallel Task Allocation in Multi-Robot Environment Under Uncertainty Based on Auction Mechanism

Abstract

Parallel task allocation in multi-robot task allocation is essential across industries, optimizing efficiency, resource management, and performance. It impacts productivity, cost-effectiveness, and safety in real-world applications such as manufacturing, agriculture, emergency response, e-commerce, and space exploration. In this paper, we propose an auction-based approach for parallel task allocation in a multi-robot environment. We introduce an approach based on a multi-stage iterative combinatorial auction to create a parallel task allocation schedule, considering task preferences and avoiding path collisions. In this paper, we mainly focus on resolving real-time conflicts and avoiding collusion during task allocation and execution. We also consider task prioritization, task importance, and deadline which can be helpful for allocating and executing tasks in the multi-robot environment.