External Axis Joint Sequencing for Collision-Free Robot Motion
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Solution Overview
Problem
Current methods for determining joint values of external axes in industrial robots with moving bases or parts are inefficient and often result in non-collision-free solutions, relying on manual trial-and-error approaches that are time-consuming and prone to errors.
Innovation Solution
A method that generates weight factor tables representing the combined effort of robot and external axis motion to optimize joint values, ensuring collision-free trajectories and minimizing production cycle time and energy consumption, using virtual simulations and optimization algorithms like simulated annealing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual trial-and-error approaches are used to determine joint values of external axes, then expert users can reach solutions, but the solutions are inefficient and often not collision-free
Solution Approach 1:
The patent replaces manual mechanical trial-and-error adjustment with an automated computational system that uses virtual simulations and optimization algorithms to determine joint values. The system substitutes human expert manual operations with automated software that processes kinematic information and calculates optimal trajectories, eliminating the inefficiency and collision risks of manual approaches.
Solution Approach 2:
The patent performs preliminary virtual simulations and collision checks before actual robot execution. By pre-calculating joint values through weight factor tables and validating trajectories in a virtual environment, the system ensures collision-free paths are established beforehand, preventing collisions during actual production operations.
2Productivity
If automated optimization algorithms are used to minimize production cycle time, then efficiency is improved, but collision detection complexity increases
Solution Approach 1:
The patent segments the optimization problem into discrete components: weight factor tables for different joint configurations, individual trajectory validations, and step-by-step collision checks. By dividing the complex optimization into manageable segments that can be independently calculated and validated, the system reduces overall complexity while maintaining optimization effectiveness.
Solution Approach 2:
The patent creates a virtual copy of the robot system and external axes for simulation purposes. All collision detection and trajectory validation operations are performed on this virtual model rather than the physical system, allowing complex optimization algorithms to run without risking actual equipment and simplifying the implementation of sophisticated optimization methods.
3Measurement precision
If weight factor tables are generated for all available configurations, then optimal joint values can be determined, but computational effort and memory requirements increase
Solution Approach 1:
The patent applies local quality by generating weight factor tables specifically for configurations that are locally relevant to the current target location and robot pose. Rather than pre-calculating all possible configurations globally, the system generates tables locally as needed based on the specific operational context, reducing unnecessary data storage while maintaining optimization precision for actual operating conditions.
Data Source
AI summary
Systems and a method determine a sequence of joint values of an external axis along a sequence of targets. Inputs are received, including robot representation, tool representation, sequence of targets, kinematics of the axis joints, and/or type of robot-axis motion. For each target, it is generated at least one weight factor table representing, for each available configuration of the axis joint motion, a combined effort of the robot motion and the axis motion depending on the type of combined robot-axis motion. Valid weight factor values of the table are determined by simulating collision free trajectories for reaching the target. The sequence of joint values of the at least one external axis is determined by finding from the weight factor table a sequence of joint values for which the sum of their corresponding weight factors for reaching the target location sequence is minimized.