Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles.

S M Yang,Y A Lin

doi:10.3390/s21062244

Abstract

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

Highlights

According to the World Health Organization, there are 1.35 million fatalities due to traffic accidents each year [1]
An improved Rapidly Exploring Random Trees (RRT) algorithm was developed for the path planning of autonomous vehicles in static obstacle avoidance
Simulation results show that the improved RRT algorithm can plan a collision-free, safe path from the start to the destination in multiple obstacle environments

Summary

Introduction

According to the World Health Organization, there are 1.35 million fatalities due to traffic accidents each year [1]. Hybrid path planning that employed fuzzy logic in decision-making was applied to generate virtual waypoints for path optimization [17], and another hybrid path planning approach combining a potential field with a sigmoid curve was proposed to improve vehicle stability and ride comfortability [18]; they remain limited to numerical simulation. If xnew ⊂ X f ree , the tree expansion continues, and the algorithm connects xnear to xnew and checks if the connection collides with any obstacle Xobs If it does, the algorithm restarts; otherwise, xnew is added to the tree as a new point, and the search repeats until the tree reaches the destination or the number of iterations of expanding the tree reaches the limit.

Illustration

Improved

Experimental Verification by Tracking Control

Findings

Conclusions