Distributed job allocation using response threshold for heterogeneous robot team under deadline constraints

Abstract

Job allocation among the robots is a significant challenge in a multi-robot environment. This article proposes a distributed algorithm for job allocation with deadline constraints. It works utilizing the response threshold model inspired by job distribution among insect societies in nature. The team divides the jobs among themselves based on their threshold capacity. The proposed approach focus on maximizing the total jobs completed with the available resources. The static case where the robots never fail is considered first for evaluating the algorithm. Then it is run in a dynamic environment with robot failure to study the effects on job allocation. Two experimental studies are conducted with varying amounts of resources. In static and dynamic cases under experimental study I, the jobs completion percentage increased by an average of 37.6% and 35.8%, respectively, over the existing method. Under experimental study II, the proposed algorithm completed all the jobs with improved resource utilization. The results show that the proposed algorithm achieves better jobs completion. The algorithm is then tested in a real-world experimental environment using FireBird V mobile robots under static and dynamic cases. The outcomes indicate that the proposed approach is efficient in job allocation in practical circumstances.