Dynamic Planning for Obstacle Avoidance of Crawler Based on Gaussian Model

Abstract

In order to solve the problems of obstacle avoidance planning and trajectory optimization of unmanned platform in hilly orchards, this paper proposes a dynamic planning algorithm based on Gaussian probability model to predict dynamic obstacle behavior. The technical route and method of theoretical derivation-algorithm design-simulation analysis-real vehicle testing are both adopted. Firstly, the global reference path of orchard is optimized based on the quadratic programming. Secondly, the intentions of surrounding pedestrians and other dynamic obstacles are estimated based on the Gaussian probability model. Subsequently, a dynamic grid map is created based on the Frenet coordinate system. Then, the obstacles avoidance paths are dynamically planned to obtain discrete optimal paths. Finally, the quintic polynomial curve is utilized to connect the sampling points to generate the path with continuous curvature. The analysis by ROS/Rviz simulation indicates that the planning time decreases by 57.97%, the number of sampled nodes decreases by 79.57%, and the curvature is smoother and continuous compared with the RRT algorithm. A field test is conducted on a hilly orchard road. With the Gaussian model, the algorithm can effectively predict the behavioral state of dynamic obstacles. The platform can autonomously plan a continuous, smooth and safe driving route in a shorter period of time, effectively avoiding obstacles and reducing the impact of steering-in-place on operating efficiency. Through the research, a dynamic obstacle behavior prediction and obstacle avoidance algorithm is proposed for the crawler chassis in hilly orchards based on the Gaussian probability model and dynamic programming, and the obstacle avoidance curve obtained by the algorithm satisfies the kinematic constraints of the crawler chassis, and has good safety, better real-time and enforceability. Additionally, a comprehensive set of software and hardware solutions for spontaneous static obstacle avoidance planning in hilly orchards is presented.