Robotic Arm Path Planning with Directional RRT Sampling
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Solution Overview
Problem
Existing robotic arm path planning methods face issues with blind sampling nodes, excessive nodes, repeated sampling in space, and high time complexity, particularly in complex environments.
Innovation Solution
A directionally extended RRT algorithm that includes determining a workspace, setting a bias probability, adjusting direction and step length for random nodes, and optimizing the path using a cubic B-spline curve to eliminate redundant nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional RRT algorithm is used for path planning, then the algorithm can handle high-dimensional spaces and complex environments, but the number of sampling nodes increases excessively and the computation time becomes too long
Solution Approach 1:
The patent applies local quality by making the sampling process non-uniform: dense sampling is performed in regions closer to the target node while sparse sampling is performed in distant regions. This is achieved through distance-based probability weighting where nodes closer to the target have higher sampling probability, thereby concentrating computational resources in critical areas and reducing overall computation time while maintaining completeness in high-dimensional spaces.
Solution Approach 2:
The patent introduces dynamic sampling parameters that adapt during the path planning process. The sampling distribution dynamically changes based on the current tree structure and distance to target, with the sampling probability function adjusting the concentration of samples based on current progress. This dynamic adaptation allows the algorithm to efficiently explore promising regions while avoiding redundant sampling in already-explored areas.
2Reliability
If traditional RRT algorithm is used for path planning, then the algorithm can find paths in complex environments, but the sampling is blind and repeats sampling in the same space repeatedly
Solution Approach 1:
The patent implements feedback mechanisms where the sampling distribution is continuously updated based on information from previously sampled nodes and the evolving tree structure. The algorithm uses distance-to-target feedback and collision information to adjust sampling probabilities, ensuring that regions that have been thoroughly explored receive lower sampling probability while unexplored or promising regions receive higher probability, thereby eliminating blind repeated sampling.
Solution Approach 2:
The patent performs preliminary action by pre-calculating distance metrics and organizing space into regions before the actual path planning begins. The sampling function is pre-configured with distance-based probability weights that guide the sampling process from the outset, preventing blind random sampling and directing exploration toward promising regions earlier in the process.
3Manufacturing precision
If the number of sampling nodes is increased to improve path planning accuracy, then the path quality improves, but the time complexity increases significantly
Solution Approach 1:
The patent changes the parameter distribution of sampling nodes rather than uniformly increasing the number of nodes. By modifying the probability distribution parameter (distance-based weighting), the algorithm achieves better path planning accuracy with fewer nodes. The parameter change transforms the sampling from uniform random to non-uniform targeted sampling, improving accuracy without proportionally increasing time complexity.
Data Source
AI summary
A robotic arm path planning method based on a Direction Extended Rapidly-exploring Random Tree (RRT) algorithm includes: determining a workspace of a robotic arm; determining a starting node and a target node based on the workspace, and performing modeling using an initialized random tree to obtain an obstacle space; generating a random node in the obstacle space, and setting a bias probability; determining whether a current random node collides with an obstacle; if yes, obtaining a new random node, until a currently generated random node does not collide with an obstacle; if not, determining whether the current random node is the target node; if the current random node is not the target node, continuing to generate new random nodes; if the current random node is the target node, obtaining an initial path; and optimizing the initial path to obtain a final path.