Learning Based Trajectory Planning and Safe Obstacle Avoidance for Tracked Vehicle Under Imprecise Model
- Resource Type
- Conference
- Authors
- Zhang, Ke; Cai, Fenghuang; Huang, Jie
- Source
- 2023 China Automation Congress (CAC) Automation Congress (CAC), 2023 China. :8748-8753 Nov, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Uncertain systems
Target tracking
Trajectory planning
Roads
Safety
Collision avoidance
Vehicle dynamics
Tracked vehicle
model predictive control
control barrier function
Gaussian regression process
trajectory planning
safe obstacle avoidance
- Language
- ISSN
- 2688-0938
A trajectory planning method based on Gaussian regression for Model Predictive Control is proposed to address the safety obstacle avoidance problem of tracked vehicle on unstructured roads. The proposed method takes into account the parameter uncertainty caused by different road conditions and slopes, with the goal of ensuring safe obstacle avoidance. In order to achieve safe obstacle avoidance in imprecise model situations, Gaussian regression process is used to learn the average model error caused by time-varying parameters, and variance is introduced in the design of control barrier function. The characteristic of this method is to increase constraints on the control barrier area to ensure safety. Simulation shows that the designed algorithm can achieve safe obstacle avoidance while reaching the target point.