Il Hun Ryu,
Thae Song Pak,
Jong Chol Kil,
Chun Nam Kim,
- Research Scholar, Department of Mechanical Engineering, Kim Chaek University of Technology, Pyongyang, DPR Korea, Korea
- Research Scholar, Department of Mechanical Engineering, Kim Chaek University of Technology, Pyongyang, DPR Korea, Korea
- Research Scholar, Department of Mechanical Engineering, Kim Chaek University of Technology, Pyongyang, DPR Korea, Korea
- Research Scholar, Department of Mechanical Engineering, Kim Chaek University of Technology, Pyongyang, DPR Korea, Korea
Abstract
Given partial knowledge about its environment (obstacle, road…) and goal position, optimal path and trajectory planning is a key problem for the control of unmanned vehicles. In path planning problem, it is very important to consider the dynamic limits of the vehicle with optimization function. The path planning was implemented by applying optimization methods based on the given obstacle information(virtual or measured by sensor system) and numerical map without considering vehicle dynamic characters. Then, considering the dynamic characters, trajectory planning was carried out to generate the trajectory that the vehicle can actually travels. This method has certain errors and some shortcomings in finding the optimal solution because the path planning and trajectory planning are carried out independently of each other. One way to solve this problem is to combine these two subjects and to find a global optimal solution. Thus, during the course of the path planning, the constraints necessary for the trajectory planning are considered and the optimization function is chosen accordingly. This paper describes a hybridized algorithm of path planning and trajectory planning, in which optimization function is determined for shortest-length, shortest-time, minimum speed variation path, and the vehicle dynamics characters are considered reasonably. Longitudinal and Lateral Vehicle dynamic models are applied and solutions are solved using genetic algorithm. Also, it is analyzed the effect of weight coefficients to the path planning and trajectory planning. This paper gives practical and useful helps to the engineers and researchers who are trying for the autonomous navigation of the unmanned ground vehicle
Keywords: Unmanned ground vehicle, path planning, trajectory planning, vehicle dynamic model
[This article belongs to International Journal of Robotics and Automation in Mechanics ]
Il Hun Ryu, Thae Song Pak, Jong Chol Kil, Chun Nam Kim. Path and Trajectory Planning of Unmanned Ground Vehicles with Vehicle Dynamic Characters. International Journal of Robotics and Automation in Mechanics. 2024; 02(02):7-14.
Il Hun Ryu, Thae Song Pak, Jong Chol Kil, Chun Nam Kim. Path and Trajectory Planning of Unmanned Ground Vehicles with Vehicle Dynamic Characters. International Journal of Robotics and Automation in Mechanics. 2024; 02(02):7-14. Available from: https://journals.stmjournals.com/ijram/article=2024/view=189767
References
- Yulong Cao, Ningfei Wang, Chaowei Xiao, “Invisible for both Camera and LiDAR: Security ofMulti-Sensor Fusion based Perception inAutonomous Driving Under Physical-World Attacks,” 2021 IEEE Symposium on Security and Privacy(SP),978-1-7281-8934-5/21.
- César Debeunne, Damien Vivet, “A Review of Visual-LiDAR Fusion basedSimultaneous Localization and Mapping,” Sensors 2020, 20, 2068.
- Yan Ren, Jiayong Lui, “Automatic Obstacle Avoidance Path Planning Method forUnmanned Ground Vehicle Based on Improved Bee ColonyAlgorithm,” Jordan Journal of Mechanical and Industrial Engineering, vol.16, no1, pp.11-18, 2021.
- A. Ramadhan, A. Al-Mayyahi, M.T. Rashid, “Path Planning and Obstacles Avoidance in DynamicWorkspace Using Polygon Shape Tangents Algorithm,” Iraqi Journal for Electrical and Electronic Engineering, vol.17, Issue 1, June 2021.
- T. Rashid, A.A. Ali, “Path planning with obstacle avoidance based on visibility binary tree algorithm,” Robotics and Autonomous Systems 61, pp.1440-1449, 2013
- Huang, S.-K.; Wang, W.-J.; Sun, C-H, “A Path Planning Strategy for Multi-Robot Moving withPath-Priority Order Based on a Generalized Voronoi Diagram,” Applied Sciences, 2021, 11, 9650.
- Hui Hu, Yuge Wang, Wenjie Tong, Jiao Zhao, Yulei Gu, “Path Planning for Autonomous Vehicles in Unknown DynamicEnvironment Based on Deep Reinforcement Learning,” Applied Sciences, 2023, 13, 10056.
- Haoxuan Li, Daoxiong Gong, Jianjun Yu,“An obstacles avoidance method for serial manipulator basedon reinforcement learning and Artificial Potential Field,” International Journal of Intelligent Robotics and Applications, 2021.
Volume | 02 |
Issue | 02 |
Received | 25/09/2024 |
Accepted | 28/11/2024 |
Published | 17/12/2024 |