TY - GEN
T1 - Comparison and Improvement of Local Planners on ROS for Narrow Passages
AU - Yuan, Huajun
AU - Li, Hanlin
AU - Zhang, Yuhan
AU - Du, Shuang
AU - Yu, Limin
AU - Wang, Xinheng
N1 - Funding Information:
ACKNOWLEDGMENT We would like to appreciate the suggestions for academic writing from Dr. Tongpo Zhang. As well, we are grateful for the support from Yue Zhang, Taoyu Wu, and Dr. Haocheng Zhao. We also would like to thank Suzhou Inteleizhen Intelligent Technology Co. Ltd and Xi’an Jiaotong-Liverpool University for their financial support to conduct this research by Key Program Special Fund in XJTLU under project KSF-E-64, XJTLU Research Development Fund under projects RDF-19-01-14 and RDF-20-01-15, and the National Natural Science Foundation of China (NSFC) under grant 52175030.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/12/10
Y1 - 2022/12/10
N2 - Path planning is challenging in various extreme and complex environments due to the explosive growth of autonomous mobile robots. The widely used Dynamic Window Approach (DWA) and Time Elastic Band (TEB) local planners are integrated into Robot Operating System (ROS). A great quantity of their parameters has a significant impact on the actual performance of planning. Aiming at the scenario of robots passing through narrow passages, this work compared and optimized the typical DWA and TEB. The results of simulations showed that the improved DWA and TEB local path planners both have better performance with gentler fluctuations of velocity and smoother paths of travel in continuous narrow passages. In addition, the number of collisions was reduced to zero and the time consumption of completing navigation was decreased by an average of 15% and 7%, respectively. Moreover, the general strategies and suggestions for adjusting related parameters of DWA and TEB through narrow passages were given in this paper. Finally, the adjusted TEB local planner was applied to the customized robot in a real similar environment, and it accomplished the navigation stably with a smooth path. The results of real experiments proved the validity and reliability of our work further.
AB - Path planning is challenging in various extreme and complex environments due to the explosive growth of autonomous mobile robots. The widely used Dynamic Window Approach (DWA) and Time Elastic Band (TEB) local planners are integrated into Robot Operating System (ROS). A great quantity of their parameters has a significant impact on the actual performance of planning. Aiming at the scenario of robots passing through narrow passages, this work compared and optimized the typical DWA and TEB. The results of simulations showed that the improved DWA and TEB local path planners both have better performance with gentler fluctuations of velocity and smoother paths of travel in continuous narrow passages. In addition, the number of collisions was reduced to zero and the time consumption of completing navigation was decreased by an average of 15% and 7%, respectively. Moreover, the general strategies and suggestions for adjusting related parameters of DWA and TEB through narrow passages were given in this paper. Finally, the adjusted TEB local planner was applied to the customized robot in a real similar environment, and it accomplished the navigation stably with a smooth path. The results of real experiments proved the validity and reliability of our work further.
KW - dynamic window approach (DWA)
KW - local path planning
KW - robot navigation
KW - simultaneous localization and mapping (SLAM)
KW - time elastic band (TEB)
UR - http://www.scopus.com/inward/record.url?scp=85146432971&partnerID=8YFLogxK
U2 - 10.1109/HDIS56859.2022.9991270
DO - 10.1109/HDIS56859.2022.9991270
M3 - Conference Proceeding
AN - SCOPUS:85146432971
T3 - 2022 International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022
SP - 125
EP - 130
BT - 2022 International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022
Y2 - 10 December 2022 through 11 December 2022
ER -