Economic speed planning for autonomous electric truck at mining site

Han Li; Yejing Zhang; Guizhen Yu; Peng Chen; Bin Zhou

doi:10.1117/12.3014533

8 December 2023 Economic speed planning for autonomous electric truck at mining site

Han Li, Yejing Zhang, Guizhen Yu, Peng Chen, Bin Zhou

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430I (2023) https://doi.org/10.1117/12.3014533
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

With the process of the electric truck, its application at mining site has attracted increasing attention. Transporting goods with autonomous electric trucks can significantly improve the energy saving in which speed planning plays a key role. This work proposes a method to plan economic speed trajectories. This method first develops a novel state-space model to capture vehicle station, speed, acceleration, and jerk. Moreover, the speed planning problem is then formulated into a quartic problem. By comprehensively utilizing the information on road topography and the regenerative braking system, an economic speed profile can be obtained. The proposed method has been tested and validated in an autonomous electric truck in real mining site environments. Its performance has been evaluated both in terms of the quality of the computed result and with respect to the required computing time. The experiment results show that the energy consumption by using the proposed method is reduced and the regenerative braking system is fully leveraged.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Han Li, Yejing Zhang, Guizhen Yu, Peng Chen, and Bin Zhou "Economic speed planning for autonomous electric truck at mining site", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430I (8 December 2023); https://doi.org/10.1117/12.3014533

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