Planning and Scheduling for Large-Scale Robot Networks: An Efficient and Comprehensive Approach
Zhe Liu, Yanzi Miao, Wei Dai, Yu Jia Zhai · 2020
Mobile robot groups incorporated with intelligence control are known to be efficient and useful in logistic, industry and public transportation implementations, serving for the field information acquisition, industrial material delivery, and passenger transportation needs. In these applications, the environments are typically dynamic and contains large number of uncertainties which will definitely affect the motion of the robot. A comprehensive solution for the task allocating and motion scheduling is of great importance. In addition, most of the existing approaches assume that the group level task allocating and the low level robot scheduling are fully independent of each other. Aiming to address these issues, in this paper, we investigate the integrative strategy which provides the synthetic key to connect task allocating with motion scheduling in an efficient and comprehensive manner. Numerical simulations with hundreds of unmanned logistic vehicles validate the effectiveness of the proposed integrative algorithms.