Robot Motion Parameter Tuning With Multi-Objective Solution Mapping
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Solution Overview
Problem
Conventional techniques optimize only a single evaluation indicator, making it difficult to assist users in selecting one optimal solution from a plurality of optimal solutions, particularly in robot motion optimization where both task performance and safety indicators need to be considered.
Innovation Solution
An information processing method that optimizes two or more evaluation indicators by dividing robot motion into segments and using multi-objective Bayesian optimization to calculate a plurality of optimal solutions, displaying them on a coordinate axis, and allowing user selection from a solution display image, accompanied by reference information based on the selected motion's history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If optimization processing is performed on a single evaluation indicator, then the optimization process is simple and fast, but it is difficult to assist users in selecting one optimal solution from multiple optimal solutions when multiple indicators need to be considered
Solution Approach 1:
The patent extends the optimization from a single-dimensional evaluation to multi-dimensional evaluation by introducing multiple evaluation indicators (e.g., task performance, safety, energy consumption). This allows the system to provide comprehensive optimal solutions that consider multiple objectives simultaneously, enabling users to select solutions based on their specific needs across different dimensions.
Solution Approach 2:
The patent introduces an intermediary component that generates and presents multiple optimal solutions to users. This intermediary process includes calculating Pareto optimal solutions, presenting them in a comprehensible format, and allowing users to select their preferred solution, thereby bridging the gap between complex multi-objective optimization and user decision-making.
2Adaptability or versatility
If multiple evaluation indicators are optimized simultaneously, then comprehensive optimal solutions considering multiple objectives are provided, but the complexity of optimization processing increases
Solution Approach 1:
The patent segments the motion trajectory into multiple sub-trajectories and performs optimization on each segment separately. This segmentation approach reduces the overall complexity of multi-objective optimization by breaking it down into smaller, more manageable sub-problems that can be solved independently and then combined.
Solution Approach 2:
The patent employs parameter changes including weighting factors for different evaluation indicators and segmentation parameters for dividing trajectories. By adjusting these parameters, the system can control the complexity of optimization while maintaining the ability to optimize multiple indicators, allowing users to balance between comprehensiveness and computational burden.
Data Source
AI summary
An information processing device acquires trajectory information about a trajectory of a motion of a robot, adjusts parameters of the robot for optimizing two or more evaluation indicators for evaluating the motion of the robot, based on the trajectory information to calculate a plurality of optimal solutions of the two or more evaluation indicators, outputs a solution display image in which the calculated plurality of optimal solutions are rendered on a plane or in a space having the two or more evaluation indicators as a coordinate axis, acquires at least one optimal solution selected by a user from the plurality of optimal solutions displayed in the solution display image, and outputs reference information based on a history of the motion of the robot, the motion corresponding to the at least one optimal solution that has been acquired.