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An Efficient Scheduling and Navigation Approach for Warehouse Multi-Mobile Robots

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Advances in Swarm Intelligence (ICSI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13345))

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Abstract

Multi-robot scheduling and navigation methods are critical for efficient warehouse handling. In this paper, we propose a Robot Operating System (ROS) based scheduling and navigation method for multi-mobile robots. In order to solve the problem of multi-robot multi-task point assignment in the warehouse environment, we establish a target model that minimizes the total transportation time and propose a hierarchical Genetic Algorithm-Ant Colony Optimization algorithm. By repeating the upper and lower operations, the shortest total transport time allocation scheme for multi-robot multi-tasking can be obtained. In order to realize the multi-robot path planning after task assignment, a multi-robot communication system is designed on the basis of ROS, and the autonomous navigation of mobile robots is employed with the help of SLAM map. The experimental results show that the proposed multi-robot scheduling method can effectively reduce the overall transportation time, realize the reasonable allocation of multi-robots and multi-tasks, and successfully complete the cargo transportation task.

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Acknowledgment

This work was supported by National Natural Science Foundation of China (No. 61876024), and partly by the higher education colleges in Jiangsu province (No. 21KJA510003), and Suzhou municipal science and technology plan project (No. SYG202129).

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Correspondence to Benlian Xu .

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Zhao, K., Xu, B., Lu, M., Shi, J., Li, Z. (2022). An Efficient Scheduling and Navigation Approach for Warehouse Multi-Mobile Robots. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-09726-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09725-6

  • Online ISBN: 978-3-031-09726-3

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