Ray Casting and Diffusion: A Fast and Efficient Sampling-Based Approach for Path Planning

This poster introduces a novel bi-directional randomized sampling-based path planning algorithm coined as Ray Casting and Diffusion (RCD). RCD models both the robot and the target as light point-sources that emit pseudo-rays towards random directions. Upon collision with obstacles or same source produced rays (robot or target), a new source point is established and a weight coefficient is assigned to it that is used in subsequent iterations. The primary concept of RCD is to iteratively repeat this process until an intersection between the robot-generated-rays and the target-generated-rays occurs (path found). RCD incorporates multiple optimizations within each layer, contributing to the proposed approach's speed, reliability, probabilistic completeness, and its ability to consistently generate feasible and efficient paths. Both the implementation and the experimental datasets have been released as an open-source project to support future research endeavors. Extensive evaluations of RCD have been conducted, comparing it against multiple well-established state-of-the-art sampling-based path planning approaches. The results indicate superior efficiency in finding paths faster compared to existing methods. In some instances, RCD yields slightly longer paths, however its overall performance showcases significant advantages in terms of speed and efficacy in path finding tasks.