Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)
Author:
Roy Rohit1, Tu You-Peng1, Sheu Long-Jye2, Chieng Wei-Hua1, Tang Li-Chuan1ORCID, Ismail Hasan3ORCID
Affiliation:
1. Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan 2. Department of Mechanical Engineering, Chung Hua University, Hsinchu 30012, Taiwan 3. Jurusen Teknik Mesin, Universitas Negeri Malang, Malang 55165, Indonesia
Abstract
Indoor mobile robot (IMR) motion control for e-SLAM techniques with limited sensors, i.e., only LiDAR, is proposed in this research. The path was initially generated from simple floor plans constructed by the IMR exploration. The path planning starts from the vertices which can be traveled through, proceeds to the velocity planning on both cornering and linear motion, and reaches the interpolated discrete points joining the vertices. The IMR recognizes its location and environment gradually from the LiDAR data. The study imposes the upper rings of the LiDAR image to perform localization while the lower rings are for obstacle detection. The IMR must travel through a series of featured vertices and perform the path planning further generating an integrated LiDAR image. A considerable challenge is that the LiDAR data are the only source to be compared with the path planned according to the floor map. Certain changes still need to be adapted into, for example, the distance precision with relevance to the floor map and the IMR deviation in order to avoid obstacles on the path. The LiDAR setting and IMR speed regulation account for a critical issue. The study contributed to integrating a step-by-step procedure of implementing path planning and motion control using solely the LiDAR data along with the integration of various pieces of software. The control strategy is thus improved while experimenting with various proportional control gains for position, orientation, and velocity of the LiDAR in the IMR.
Funder
National Science and Technology Council, R.O.C.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference36 articles.
1. Betz, J., Wischnewski, A., Heilmeier, A., Nobis, F., Stahl, T., Hermansdorfer, L., Lohmann, B., and Lienkamp, M. (2018, January 26). What can we learn from autonomous level-5 motorsport?. Proceedings of the 9th International Munich Chassis Symposium, Wiesbaden, Germany. 2. Ismail, H., Roy, R., Sheu, L.J., Chieng, W.H., and Tang, L.C. (2022). Exploration-Based SLAM (e-SLAM) for the Indoor Mobile Robot Using Lidar. Sensors, 4. 3. Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving;Bresson;IEEE Trans. Intell. Veh.,2017 4. Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F., and Sayd, P. (2006, January 17–22). Real time localization and 3D reconstruction. Proceedings of the IEEE CVPR, New York, NY, USA. 5. ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM;Campos;IEEE Trans. Robot.,2021
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|