Robot Arm Trajectory Generation for Time and Constraint Optimization
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Solution Overview
Problem
Existing methods for generating robot arm trajectories fail to optimize operation time and satisfy constraint conditions such as joint torque, angular velocity, and interference avoidance, leading to inefficient cycle times and increased manual intervention.
Innovation Solution
A method and apparatus that generate multiple trajectories, evaluate and select the best one based on evaluation values, and update the trajectory through iterative processes to optimize for operation time and constraint satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If teaching points are prepared manually in trial and error manner to avoid interference and inoperable areas, then the robot arm can operate without interference, but the cycle time is delayed and manual work is required
Solution Approach 1:
The patent replaces manual trial-and-error teaching point preparation with an automated computer-based simulation system. The simulation system uses virtual models of the robot arm and peripheral devices to automatically generate and evaluate trajectories, substituting human operators with computational algorithms that can assess multiple trajectories rapidly and select optimal ones based on evaluation criteria.
Solution Approach 2:
The patent performs preliminary simulation and evaluation of multiple trajectories before actual robot operation. By pre-calculating evaluation values for each trajectory segment and selecting the optimal trajectory in advance, the system avoids interference issues during real operation and eliminates the need for manual trial-and-error adjustments, thereby reducing cycle time.
2Device complexity
If the number of intermediate command values is determined in advance as a constant, then the trajectory generation is simplified, but the operation time cannot be optimized
Solution Approach 1:
The patent makes the number of intermediate command values dynamic rather than constant. The simulation system automatically determines the appropriate number of intermediate command values based on the specific trajectory requirements, allowing flexibility to optimize operation time for different motion scenarios while maintaining manageable complexity through automated adjustment.
Solution Approach 2:
The patent changes the parameter of intermediate command values from a fixed constant to a variable that is automatically adjusted by the simulation system. By evaluating multiple trajectories with different numbers of intermediate command values and selecting the optimal one, the system optimizes operation time while keeping the generation process manageable through systematic evaluation.
3Speed
If teaching points are reset manually to optimize trajectory, then the robot arm can operate at optimum motion speed, but man-hours increase and personal costs rise
Solution Approach 1:
The patent replaces manual trajectory optimization with an automated simulation system that uses computational algorithms to evaluate and select optimal trajectories. The system automatically adjusts teaching points to achieve optimum motion speed without requiring human operators to manually reset and re-evaluate trajectories, thereby eliminating the increase in man-hours and personal costs.
Solution Approach 2:
The simulation system performs self-evaluation and self-optimization of trajectories by automatically calculating evaluation values for multiple trajectories and selecting the optimal one. This self-service capability eliminates the need for manual intervention in trajectory optimization, allowing the system to achieve optimum motion speed without increasing preparation time or human resource requirements.
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.