Global Path Planning in Optimal Robot Path Planning

Global path planning in optimal robot path planning is a key technology used to find an optimal path from a starting point to a target destination for robots operating in complex environments. Global path planning typically considers overall environmental information, such as obstacle locations and feasible areas, to ensure path safety and efficiency.

The algorithm introduced in this article is based on a direction-weighted binary tree algorithm implemented in MATLAB. The core concept involves decomposing the search space into a binary tree structure, where each node represents a possible movement direction for the robot. Direction weights are incorporated to evaluate path quality, with weight assignments typically based on heuristic information like distance to target and obstacle avoidance costs, ensuring the algorithm prioritizes superior directions during path exploration.

The search process initiates from the starting point, progressively expanding binary tree nodes while calculating total cost for each potential direction (including path length and obstacle avoidance capability). The algorithm continues exploration along the branch with minimal cost. This hierarchical progressive approach enables efficient discovery of globally optimal or near-optimal feasible paths.

The algorithm's advantage lies in its computational efficiency, making it suitable for moderately complex environments. The incorporation of direction weights enhances path rationality, enabling robots to advance rapidly toward targets while avoiding obstacles. In MATLAB implementation, matrix operations and recursive structures can efficiently realize this algorithm, facilitating debugging and optimization processes.

Global path planning constitutes a crucial component of robot navigation. The direction-weighted binary tree algorithm provides a solution balancing computational complexity and path quality, applicable to path planning tasks in industrial robotics, autonomous driving, and related fields.