Robot Motion Planning With Reused Reference Paths for Fast Convergence
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Solution Overview
Problem
Existing robot motion planning techniques require many iterations to compute a final converged solution due to significant differences between the initial straight line path and the final path, leading to impractically long computation times in real-time environments with changing start and goal points and obstacles.
Innovation Solution
A refined initial reference path is created by selecting a previously computed path with similar start and goal points and collision avoidance constraints, and modifying it to match the new path's start and goal points, allowing for quicker convergence in optimization computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a straight line path is used as the initial reference path, then the path definition is simple, but the number of iterations required for convergence increases significantly
Solution Approach 1:
The system performs preliminary actions by selecting and adjusting a previously computed path before optimization begins. This preliminary path selection and adjustment step prepares a better starting point for the optimization algorithm, reducing the number of iterations needed to converge to the final solution.
Solution Approach 2:
The system creates a copy of a previously computed path and modifies it to serve as the initial reference path. Instead of creating a new straight line path from scratch, the system copies an existing path that is likely to be similar to the target path, thereby reducing the computational distance to the final solution.
2Manufacturing precision
If many iterations are performed to achieve convergence, then the path optimization accuracy improves, but the real-time computation requirement cannot be met
Solution Approach 1:
By performing preliminary path selection and adjustment before optimization, the system reduces the number of iterations required to achieve the desired accuracy. This preliminary preparation ensures that the optimization starts from a point close to the final solution, maintaining both accuracy and real-time performance.
Solution Approach 2:
The system changes the initial parameters of the reference path by selecting a previously computed path and adjusting its state points. This parameter change transforms the initial reference path from a simple straight line to a more informed path that requires fewer optimization iterations, thereby achieving both accuracy and speed.
3Loss of time
If a previously computed path is selected and adjusted, then the convergence speed improves, but the path selection and adjustment complexity increases
Solution Approach 1:
The system copies a previously computed path and makes simple adjustments to its state points. This copying approach leverages existing computational work while requiring minimal additional complexity for selection and adjustment, achieving fast convergence without excessive complexity.
Solution Approach 2:
The system uses its own previously computed paths as the basis for new path planning. By reusing and adjusting its own historical computation results, the system reduces the need for complex external path selection mechanisms, achieving efficient convergence with moderate complexity.
Data Source
AI summary
A robot optimization motion planning technique using a refined initial reference path. When a new path is to be computed using motion optimization, a candidate reference path is selected from storage which was previously computed and which has similar start and goal points and collision avoidance environment constraints to the new path. The candidate reference path is adjusted at all state points along its length to account for the difference between the start and goal points of the new path compared to those of the previously-computed path, to create the initial reference path. The initial reference path, adjusted to fit the start and goal points, is then used as a starting state for the motion optimization computation. By using an initial reference path which is similar to the final converged new path, the optimization computation converges more quickly than if a naïve initial reference path is used.