TY - GEN
T1 - Obstacle-Centered Trajectory Planning for Autonomous Mobile Robot
AU - Jian, Zhiqiang
AU - Zhang, Songyi
AU - Chen, Shitao
AU - Zhang, Tangyike
AU - Nan, Zhixiong
AU - Zheng, Nanning
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/19
Y1 - 2021/9/19
N2 - Trajectory planning enables Autonomous Mobile Robot (AMR) to have intelligence and avoid a collision in the interaction with obstacles. However, in scenes with multiple obstacles, most of the existing methods cannot minimize the collision risk. It is because that these methods do not distinguish the importance of the obstacles in the scene. Therefore, in this paper, we proposed an Obstacle-Centered Trajectory Planning (OCTP) method to solve the problem. In our method, a novel collision risk evaluation model is constructed, which considers the importance of each obstacle. In addition, a sliding-window-based key points interpolation method is used to smooth the velocity profile obeying constraints of collision risk and curvature. Finally, a comparison with the baseline method is performed. The experimental results show that the proposed method can effectively reduce AMR's collision risk in interacting with obstacles.
AB - Trajectory planning enables Autonomous Mobile Robot (AMR) to have intelligence and avoid a collision in the interaction with obstacles. However, in scenes with multiple obstacles, most of the existing methods cannot minimize the collision risk. It is because that these methods do not distinguish the importance of the obstacles in the scene. Therefore, in this paper, we proposed an Obstacle-Centered Trajectory Planning (OCTP) method to solve the problem. In our method, a novel collision risk evaluation model is constructed, which considers the importance of each obstacle. In addition, a sliding-window-based key points interpolation method is used to smooth the velocity profile obeying constraints of collision risk and curvature. Finally, a comparison with the baseline method is performed. The experimental results show that the proposed method can effectively reduce AMR's collision risk in interacting with obstacles.
UR - https://www.scopus.com/pages/publications/85118470669
U2 - 10.1109/ITSC48978.2021.9564740
DO - 10.1109/ITSC48978.2021.9564740
M3 - 会议稿件
AN - SCOPUS:85118470669
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 486
EP - 492
BT - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
Y2 - 19 September 2021 through 22 September 2021
ER -