TY - GEN
T1 - A Generative Adversarial Network Based Motion Planning Framework for Mobile Robots in Dynamic Human-Robot Integration Environments
AU - Kong, Yuqi
AU - Wang, Yao
AU - Hong, Yang
AU - Ye, Rongguang
AU - Chi, Wenzheng
AU - Sun, Lining
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
PY - 2022
Y1 - 2022
N2 - In the human-robot integration environment, efficient and safe navigation is of great significance for mobile service robots. At present, human-robot integration environment is highly uncertain and dynamic, which brings new challenges to motion planning. In order to solve this problem, this paper proposes a dynamic obstacle avoidance strategy based on imitation learning in a Generative Adversarial Network (GAN) framework. When the robot detects a pedestrian around it, it generates an active obstacle avoidance point that maintains an appropriate distance from the pedestrian according to the pedestrian pose and the global path planned by the A* algorithm as a sub-goal to guide the robot for motion planning. In the experiment, the performance of the algorithm is evaluated by the number of entering the pedestrian person space, the time cost and the trajectory length. Compared with the Dynamic Window Approach (DWA) and Proactive Social Motion Model (PSMM) algorithms, the experimental results show that our proposed algorithm has better performance than the other two algorithms in the human-robot integration environment.
AB - In the human-robot integration environment, efficient and safe navigation is of great significance for mobile service robots. At present, human-robot integration environment is highly uncertain and dynamic, which brings new challenges to motion planning. In order to solve this problem, this paper proposes a dynamic obstacle avoidance strategy based on imitation learning in a Generative Adversarial Network (GAN) framework. When the robot detects a pedestrian around it, it generates an active obstacle avoidance point that maintains an appropriate distance from the pedestrian according to the pedestrian pose and the global path planned by the A* algorithm as a sub-goal to guide the robot for motion planning. In the experiment, the performance of the algorithm is evaluated by the number of entering the pedestrian person space, the time cost and the trajectory length. Compared with the Dynamic Window Approach (DWA) and Proactive Social Motion Model (PSMM) algorithms, the experimental results show that our proposed algorithm has better performance than the other two algorithms in the human-robot integration environment.
KW - Dynamic obstacle avoidance
KW - Generative adversarial networks
KW - Human-robot integration environment
UR - https://www.scopus.com/pages/publications/85149878510
U2 - 10.1007/978-3-031-24667-8_38
DO - 10.1007/978-3-031-24667-8_38
M3 - 会议稿件
AN - SCOPUS:85149878510
SN - 9783031246661
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 427
EP - 439
BT - Social Robotics - 14th International Conference, ICSR 2022, Proceedings
A2 - Cavallo, Filippo
A2 - Fiorini, Laura
A2 - Sorrentino, Alessandra
A2 - Cabibihan, John-John
A2 - He, Hongsheng
A2 - Liu, Xiaorui
A2 - Matsumoto, Yoshio
A2 - Ge, Shuzhi Sam
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Social Robotics, ICSR 2022
Y2 - 13 December 2022 through 16 December 2022
ER -