Robot Arm Trajectory Planning for Obstacle-Aware Time Optimization
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Solution Overview
Problem
Existing methods for generating robot arm trajectories often result in inefficient operation times and interference with obstacles or other objects, failing to optimize for both speed and constraint satisfaction due to fixed intermediate command values and inflexible constraint handling.
Innovation Solution
A method and apparatus that generate multiple trajectories, evaluate and select optimal ones based on motion evaluation values, and iteratively update these trajectories to ensure interference avoidance and constraint satisfaction, using techniques like path planning and minimum time control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If teaching points are prepared manually in trial and error manner to avoid interference, then the robot arm does not interfere with obstacles, but the operation time increases and productivity decreases
Solution Approach 1:
The patent replaces manual trial-and-error teaching point preparation with an automated computer-based trajectory generation system. The controller automatically generates multiple candidate trajectories, evaluates them for interference and operation time, and selects the optimal trajectory without requiring manual intervention, thus resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent dynamically generates and evaluates multiple candidate trajectories instead of using a fixed manual approach. The system adaptively selects the best trajectory based on real-time evaluation of interference avoidance and operation time metrics, allowing the robot to operate efficiently while maintaining safety constraints.
2Device complexity
If fixed number of intermediate command values is used, then the trajectory generation is simple, but the operation time cannot be optimized
Solution Approach 1:
The patent changes the parameter of intermediate command values from a fixed number to a variable number that can be dynamically adjusted. The system generates multiple trajectories with different numbers of intermediate command values and selects the one that optimizes operation time, thus resolving the contradiction between simplicity and time optimization.
Solution Approach 2:
The patent makes the trajectory generation process dynamic by allowing the number of intermediate command values to vary based on optimization requirements. The controller adaptively determines the appropriate number of intermediate values needed to achieve optimal operation time while maintaining interference avoidance.
3Reliability
If manual works are required to reconsider and reset teaching points, then constraint satisfaction is improved, but the personal costs and preparation time increase
Solution Approach 1:
The patent replaces manual reconsideration and resetting of teaching points with an automated evaluation and selection process. The controller automatically evaluates multiple candidate trajectories against constraints and selects the optimal one, eliminating the need for manual intervention and reducing both preparation time and personal costs while maintaining constraint satisfaction.
Solution Approach 2:
The system performs self-evaluation and self-optimization of trajectories without requiring external manual intervention. The controller autonomously generates, evaluates, and selects trajectories based on predefined constraints and optimization criteria, making the system self-sufficient in terms of trajectory preparation.
Data Source
AI summary
A trajectory generating method includes a first generating process of generating a plurality of trajectories between a start teaching point and a target teaching point, an evaluation process of evaluating a motion of the robot arm on each trajectory to calculate an evaluation value of each trajectory, a selection process of selecting one of the plurality of trajectories based on calculated evaluation values, and an update process of updating the trajectory by repeating the processes of generating a plurality of new trajectories by changing a selected trajectory in the selection process, of calculating an evaluation value of a motion of the robot arm on each changed trajectory and of selecting a trajectory based on calculated evaluation values.