Redundant Robot Joint-Angle Control for Adaptive Pose Optimization
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Solution Overview
Problem
Existing robot control methods struggle to effectively adjust robot poses according to a set reference path and the robot's own situation, limiting the flexibility of robots in performing tasks.
Innovation Solution
A method for controlling redundant robots that involves determining an inertia matrix and a slack variable, formulating a momentum equation, obtaining reference joint angles, and optimizing an objective function to adjust joint angles and improve task flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot uses a fixed reference path for task execution, then the task completion is straightforward, but the robot cannot effectively adjust according to its own situation, reducing task flexibility
Solution Approach 1:
The patent applies dynamics by transforming the static reference path into dynamic reference joint angles through momentum equation calculation. The system calculates reference joint angles based on the robot's current state and momentum, allowing the robot to adapt its motion to its own situation while maintaining task completion. This enables effective adjustment during task execution without requiring complex real-time path replanning.
2Measurement precision
If the robot operator sets a detailed reference path, then the task execution is precise, but the robot cannot adjust according to its own situation, limiting flexibility
Solution Approach 1:
The patent implements feedback by using the robot's momentum and current state information to calculate reference joint angles. The momentum equation incorporates the robot's dynamic state, creating a feedback mechanism that allows the robot to adjust its execution while maintaining precision. This resolves the contradiction by enabling both precise task execution and adaptive adjustment based on the robot's own situation.
3Productivity
If the robot uses redundant degrees of freedom with fixed pose selection, then the task can be completed, but the robot cannot optimize its pose to improve task performance
Solution Approach 1:
The patent applies parameter changes by optimizing joint angles based on the robot's momentum and current state. Instead of fixing the pose selection among redundant degrees of freedom, the system dynamically adjusts joint angle parameters to optimize task performance. This enables the robot to select optimal poses from multiple redundant options, improving productivity without requiring complex optimization algorithms.
Data Source
AI summary
A method of controlling a robot includes: obtaining an inertia matrix and a slack variable of the robot, and determining a momentum equation of the robot according to the inertia matrix and the slack variable; obtaining reference joint angles corresponding to a reference action of the robot; determining an optimization objective function of the momentum equation according to a first preset weight coefficient of the slack variable and a second preset weight coefficient of the reference joint angles; and determining joint angles of the robot according to the optimization objective function, and driving the robot to move according to the joint angles of the robot.