Robot Motion Path Planning Using Segmented BF-RRT
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current path planning algorithms for robots, such as the RRT algorithm, often encounter local minima and inefficiencies in tree expansion, leading to increased time and reduced speed in finding optimal motion paths that avoid obstacles.
Innovation Solution
The proposed method involves recognizing intermediate points between the start and goal points, selecting waypoints with the smallest goal score, dividing the path into sections, and simultaneously expanding trees using the BF-RRT algorithm to generate an optimal path that avoids obstacles, thereby reducing the likelihood of local minima and improving path planning performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the RRT algorithm is used to expand trees in path planning, then the manipulator can reach the goal point, but local minima occur and the tree expansion takes considerable time
Solution Approach 1:
The configuration space is divided into multiple sections based on obstacles and intermediate points. Instead of expanding a single tree from the start point, multiple trees are simultaneously expanded from different start points within each section toward the goal point. This segmentation approach prevents the algorithm from getting trapped in local minima by providing multiple parallel search paths, thereby reducing the time consumed while maintaining path planning reliability.
Solution Approach 2:
Intermediate points are recognized and waypoints are selected in advance before tree expansion begins. The configuration space is pre-divided into sections based on these predetermined intermediate points. This preliminary action allows the simultaneous tree expansion to proceed more efficiently by establishing multiple search directions upfront, reducing the time required to escape local minima and find the optimal path.
2Ease of operation
If the tree is expanded to reach the goal point, then a motion path can be found, but the tree may be expanded in wrong directions until local minima are recognized
Solution Approach 1:
The configuration space is segmented into multiple sections with intermediate points and waypoints identified in advance. Multiple trees are simultaneously expanded from different start points within these sections toward the goal point. This segmentation prevents wasted expansion in wrong directions by establishing multiple valid search paths from the beginning, thereby improving search speed while maintaining the ability to find motion paths.
Solution Approach 2:
The algorithm transitions from single-tree expansion in one dimension to multi-tree simultaneous expansion across multiple dimensions (different sections and waypoints). By adding the dimension of parallel tree expansion from multiple starting positions, the algorithm efficiently explores the configuration space without getting trapped in wrong directions, thus improving productivity while preserving path finding capability.
3Device complexity
If a single tree is expanded from the start point, then the algorithm is simple to implement, but the time to search for the solution decreases the speed
Solution Approach 1:
The configuration space is divided into multiple sections with predetermined intermediate points and waypoints. Multiple trees are simultaneously expanded from different start points within these sections toward the goal point. This segmentation enables parallel processing of path search, significantly improving path planning speed while maintaining manageable algorithm complexity through systematic organization of the search process.
Solution Approach 2:
Intermediate points, waypoints, and section divisions are determined in advance before tree expansion begins. This preliminary action establishes the framework for simultaneous tree expansion, allowing the algorithm to proceed efficiently with multiple parallel searches. The pre-computed structure reduces the computational burden during execution, improving productivity without substantially increasing algorithm complexity.
Data Source
AI summary
A suitable waypoint is selected using a goal score, a section from a start point to a goal point through the waypoint is divided into a plurality of sections based on the waypoint with a solution of inverse kinematics, and trees are simultaneously expanded in the sections using a Best First Search & Rapidly Random Tree (BF-RRT) algorithm so as to generate a path. By this configuration, a probability of local minima occurring is decreased compared with the case where the waypoint is randomly selected. In addition, since the trees are simultaneously expanded in the sections each having the waypoint with a solution of inverse kinematics, the solution may be rapidly obtained. A time consumed to search for an optimal motion path may be shortened and path plan performance may be improved.