GPU-Based Parallel Path Planning for Mobile Robot Navigation in Dynamic Environments

In our modern, ever-evolving, and intricately interconnected world, the task of planning optimal paths for mobile robots has emerged as a challenge. This challenge arises from our surroundings’ growing dynamism and complexity, characterized by the continual variation of obstacles and environmental conditions. This paper proposes a new approach called the Parallel Kinematic Rapidly Exploring Random Tree (PK-RRT) algorithm for mobile robot path planning in dynamic environments. Our PK-RRT algorithm incorporates kinematic constraints when the robot moves on the RRT tree to ensure that the paths generated are feasible and smooth. It also applies the multi-threading technique in which multiple threads work in parallel to speed up the path generation process and improve its convergence. We conducted a comprehensive performance comparison with two existing algorithms, namely Bidirectional Rapidly Exploring Random Tree (Bi-RRT) and Quadratic Rapidly Exploring Random Tree (Quad-RRT). The results show that our PK-RRT algorithm reduces the computation time by 30%, improves the computational efficiency by 40%, and increases the stability by 50% compared to the existing algorithms.