Resource-Saving Navigation Algorithm Based on Random Sampling Search

TL;DR: This thesis proposes a resource-saving navigation algorithm, DBT-RRT*, for microrobot systems, integrating target biasing, bidirectional search, and dynamic step size, achieving efficient planning in complex environments with minimal computational cost.

Abstract: In recent years, micro-miniaturization, swarm intelligence, and intelligent control have become mainstream trends in robotic systems research. Micro-robot systems are gaining attention due to their flexibility, robustness, and scalability. However, limited computing and storage resources pose challenges for applying traditional path planning and collaboration algorithms in such platforms. In addition, many existing solutions assume simplified environments and reliable communication, which are often unrealistic. This thesis investigates a collaborative diffusion-based search strategy tailored for microrobot systems. To address resource limitations, a DBT-RRT* path planning algorithm is proposed. It integrates a target biasing mechanism and bidirectional search with dynamic step size, while applying the triangle inequality for post-processing path optimization. The algorithm achieves efficient planning in complex environments with minimal computational cost. Its effectiveness is validated through experiments.