README.md

Efficient Collision Detection in Sampling-Based Path Planning via Candidate Obstacle Filtering by Sorting Axis-Aligned Boundaries

Project Report | Presentation slide

Overview

This is the repository for the project of course EEE5058 Introduction to Information Technology in SUSTech 2023-Spring.

Abstract in the report:

Collision detection is considered to be the most expensive computational bottleneck in sampling-based path planning algorithms. In this report, a simple and efficient collision detection mechanism is proposed, which performs binary search based on sorted axis-aligned boundaries to obtain candidate obstacles, thus avoiding collision detection for obstacles that will never collide. This mechanism is referred to as the Candidate Obstacle Filtering (COF) mechanism in the following. The key idea of COF mechanism is that an obstacle whose right boundary is on the left side of the left boundary of the object must not collide with the object, and similarly for the left boundary. The path planning algorithm based on Rapidly-Exploring Random Tree was constructed to be used for experiments in the random world map, while the brute-force collision detection and the proposed candidate obstacle filtering collision detection mechanism were implemented separately. The experiments show that the proposed mechanism optimizes the number of obstacles to be detected in a single collision detection from O(n) to O(1), and the additional time overhead is negligible. The resulting performance improvement is proportional to the individual obstacle collision detection time, the number of obstacles, and the number of collision detection executions. The experimental code is available at https://github.com/Ryyyc/EEE5058-Introduction-to-Information-Technology.

Run Demo

Map 01

python main.py --map=1

Output

num_obstacle:  15
num_obstacles_checked_avg: RRT  14.492895639392454  , RRT_COF  3.1293483586477215
Path Found :  True , Path Same:  True
RRT path len:  322 , iter_used:  2041
Total Time: RRT  0.7256631851196289 , RRT_COF  0.6811532974243164
is_collision_free_time: RRT 0.04600, RRT_COF 0.03900, RRT_COF/RRT: 0.84783, speedup: 0.15217

Visualization

Map02

python main.py --map=2

Output

num_obstacle:  15
num_obstacles_checked_avg: RRT  3.052924461283448  , RRT_COF  0.7648591049017286
Path Found :  True , Path Same:  True
RRT path len:  359 , iter_used:  8446
Total Time: RRT  1.1020424365997314 , RRT_COF  1.0637059211730957
is_collision_free_time: RRT 0.12700, RRT_COF 0.09100, RRT_COF/RRT: 0.71654, speedup: 0.28346

Visualization

Experiments Result in the Random World Map

Timing is done using the Python Profiler cProfile, and the code used is in script profile_rrt.py.

more details in report.

Acknowledgement

The Rapidly-Exploring Random Tree (RRT) algorithm is implemented with reference to AtsushiSakai/PythonRobotics.