Hybrid SUSD-Based Task Allocation for Heterogeneous Multi-Robot Teams
Shengkang Chen, Tony X. Lin, Said Al-Abri, Ronald C. Arkin, Fumin Zhang
Abstract
Effective task allocation is an essential component to the coordination of heterogeneous robots. This paper proposes a hybrid task allocation algorithm that improves upon given initial solutions, for example from the popular decentralized market-based allocation algorithm, via a derivative-free optimization strategy called Speeding-Up and Slowing-Down (SUSD). Based on the initial solutions, SUSD performs a search to find an improved task assignment. Unique to our strategy is the ability to apply a gradient-like search to solve a classical integer-programming problem. The proposed strategy outperforms other state-of-the-art algorithms in terms of total task utility and can achieve near optimal solutions in simulation. Experimental results using the Robotarium are also provided.
BibTeX
@inproceedings{icra2023_hybridsusdbasedt,
title = {Hybrid SUSD-Based Task Allocation for Heterogeneous Multi-Robot Teams},
author = {Shengkang Chen and Tony X. Lin and Said Al-Abri and Ronald C. Arkin and Fumin Zhang},
booktitle = {ICRA 2023},
year = {2023}
}