Adaptive Evolutionary Algorithm for Mechanical Arm Motion Path Planning
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Solution Overview
Problem
Existing methods for determining the motion path of mechanical arms are either too time-consuming with high accuracy using multi-objective evolutionary algorithms or prone to large errors with single-objective algorithms, leading to resource wastage and inefficiency.
Innovation Solution
A method that establishes a kinematics model and constructs optimization functions for lateral and longitudinal error minimization, adaptively selecting between single-objective and multi-objective evolutionary algorithms based on monotonicity thresholds to determine the optimal motion path of a mechanical arm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-objective evolutionary algorithm is used, then accuracy of motion path planning is improved, but computation time increases and resources are wasted
Solution Approach 1:
The patent applies dynamics by making the optimization algorithm adaptive rather than static. The system dynamically selects between single-objective and multi-objective evolutionary algorithms based on the monotonicity of the objective function, allowing the computation method to change according to the problem characteristics. This resolves the contradiction by using the simpler single-objective algorithm when applicable (reducing time) while resorting to the more accurate multi-objective algorithm only when necessary (maintaining accuracy).
Solution Approach 2:
The patent changes the parameter of algorithm selection based on the monotonicity threshold of the objective function. By evaluating whether the objective function is monotonic and selecting the appropriate algorithm type accordingly, the system optimizes the balance between computation time and planning accuracy. This parameter-based selection resolves the contradiction by adapting the algorithm complexity to the problem characteristics.
2Loss of time
If single-objective evolutionary algorithm is used, then computation time is reduced, but accuracy of motion path planning deteriorates
Solution Approach 1:
The system dynamically adjusts the algorithm selection based on the monotonicity evaluation of the objective function. When the function is monotonic, the simpler single-objective algorithm is used (saving time); when non-monotonic, the more accurate multi-objective algorithm is selected. This dynamic adaptation resolves the contradiction by matching algorithm complexity to problem characteristics.
Solution Approach 2:
The patent changes the algorithm type parameter based on the monotonicity threshold evaluation. By switching between single-objective and multi-objective approaches depending on the objective function properties, the system achieves time efficiency when possible while maintaining accuracy when required, thus resolving the contradiction.
3Measurement precision
If multi-objective evolutionary algorithm is used, then accuracy of motion path planning is improved, but device complexity increases
Solution Approach 1:
The patent segments the optimization process into two distinct paths: single-objective evolutionary algorithm for monotonic functions and multi-objective evolutionary algorithm for non-monotonic functions. This segmentation allows the system to use the simpler algorithm when applicable, reducing overall complexity while maintaining the option to use the more complex algorithm only when necessary for accuracy.
Solution Approach 2:
The system dynamically selects the algorithm complexity level based on the monotonicity evaluation. By adapting the algorithm type to the problem characteristics, the system avoids unnecessarily using the complex multi-objective algorithm when the simpler single-objective version suffices, thus resolving the contradiction between accuracy and complexity.
4Ease of operation
If single-objective evolutionary algorithm is used, then operation simplicity is improved, but accuracy of motion path planning deteriorates
Solution Approach 1:
The patent segments the operation into two simplicity levels: the simpler single-objective algorithm for monotonic cases and the more sophisticated multi-objective algorithm for non-monotonic cases. This segmentation allows the system to maintain operational simplicity when the problem characteristics permit, while ensuring accuracy when the simpler approach would fail.
Solution Approach 2:
The patent changes the algorithm selection parameter based on monotonicity evaluation, allowing the system to operate with the simpler single-objective algorithm when applicable (maintaining ease of operation) while switching to the more accurate multi-objective algorithm when needed (ensuring precision).
Data Source
AI summary
Disclosed is a method and system for determining a motion path of a mechanical arm in which, after a kinematics model of a mechanical arm is established, multiple objective optimization functions are constructed according to the model, theoretical coordinates of a tail end of the arm, and an arm length. If a number of individuals in an evolutionary population, that makes both lateral and longitudinal error optimization functions in an optimization function monotonically increase or decrease, reaches the threshold, an optimal solution is determined using a single-objective evolutionary algorithm; otherwise, a multi-objective evolutionary algorithm is used. The multi- or single-objective evolutionary algorithms can be adaptively selected according to the number of individuals in the population that makes the lateral and longitudinal error optimization functions change. Therefore, short time consumption of single-objective and high accuracy of multi-objective are combined to quickly and accurately plan a motion path of the mechanical arm.