文献类型：期刊文献

英文题名：An autonomous navigation approach for unmanned vehicle in outdoor unstructured terrain with dynamic and negative obstacles

作者：Zhou, Bo[1];Yi, Jianjun[1];Zhang, Xinke[1];Chen, Liwei[1];Yang, Ding[1];Han, Fei[2,3];Zhang, Hanmo[2,3]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]Shanghai Key Lab Aerosp Intelligent Control Techn, Shanghai 201109, Peoples R China;[3]Shanghai Aerosp Control Technol Inst, Shanghai 201109, Peoples R China

年份：2022

卷号：40

期号：8

起止页码：2831

外文期刊名：ROBOTICA

收录：;EI(收录号：20220711620223);WOS:【SCI-EXPANDED(收录号:WOS:000748271500001)】；

基金：This paper was supported by the Major Program of National Natural Science Foundation of China under GrantNo. 61690214, Shanghai Science and Technology Action Plan under GrantNo.18DZ1204000, 18510745500, 18510750100, 18510730600, Shanghai Aerospace Science and Technology Innovation Fund (SAST) under Grant No. 2019-080, 2019-116 and Shanghai Sailing Program under Grant No. 20YF1417300, and the Natural Science Fund of China (NSFC) under Grant No.51575186.

语种：英文

外文关键词：unstructured terrain; negative obstacles; unmanned vehicles; trajectory optimization

摘要：At present, the study on autonomous unmanned ground vehicle navigation in an unstructured environment is still facing great challenges and is of great significance in scenarios where search and rescue robots, planetary exploration robots, and agricultural robots are needed. In this paper, we proposed an autonomous navigation method for unstructured environments based on terrain constraints. Efficient path search and trajectory optimization on octree map are proposed to generate trajectories, which can effectively avoid various obstacles in off-road environments, such as dynamic obstacles and negative obstacles, to reach the specified destination. We have conducted empirical experiments in both simulated and real environments, and the results show that our approach achieved superior performance in dynamic obstacle avoidance tasks and mapless navigation tasks compared to the traditional 2-dimensional or 2.5-dimensional navigation methods.