Robot Motion Control With Dynamic Constraints for Singularity Avoidance
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Solution Overview
Problem
Conventional robot motion control schemes struggle to account for real-time motion status, leading to potential joint singularities and unsafe operation due to external interference, resulting in accidents.
Innovation Solution
A task execution control method that integrates real-time state information to determine task execution coefficient matrices, construct dynamic and parameter distribution constraints, and solve a task execution loss function to ensure safe and desired robot motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional robot motion control schemes are used, then the robot can perform basic motion tasks, but the robot may produce joint singularities or exceed safe motion ranges due to not accounting for real-time motion status
Solution Approach 1:
The patent implements real-time feedback by continuously monitoring the robot's motion status and dynamically adjusting control parameters. The controller receives real-time state information from sensors and uses this feedback to modify task execution coefficient matrices, ensuring the robot remains within safe motion ranges while adapting to changing conditions during operation.
Solution Approach 2:
The control system transitions from static pre-planned trajectories to dynamic real-time control. The patent dynamically determines task execution coefficient matrices based on current motion state, allowing the robot to adapt its motion plan on-the-fly to avoid singularities and unsafe configurations while maintaining task completion.
2Reliability
If real-time state information is integrated into control, then robot safety and task execution are improved, but control calculation complexity increases
Solution Approach 1:
The control calculation is segmented into modular components: obtaining real-time state information, determining task execution coefficient matrices, constructing dynamic constraints, and solving optimization objectives. This segmentation allows each module to be processed independently and efficiently, reducing overall computational complexity while maintaining comprehensive real-time control.
Solution Approach 2:
The patent changes control parameters dynamically by determining task execution coefficient matrices based on real-time motion state. Instead of using fixed control parameters, the system adjusts these parameters in real-time based on current robot configuration and task requirements, optimizing both safety and computational efficiency.
3Reliability
If dynamic constraints are constructed based on real-time motion status, then the robot avoids singularities and unsafe motion, but the control system requires more complex real-time monitoring
Solution Approach 1:
The system uses real-time feedback from motion status sensors to dynamically construct constraints that prevent the robot from entering unsafe configurations or singularities. The feedback loop continuously monitors joint positions and velocities, and adjusts constraints accordingly to maintain safe operation throughout the motion trajectory.
Data Source
AI summary
A method for controlling a robot includes: obtaining current motion state information of the robot and desired motion trajectory information corresponding to a target task; determining task execution coefficient matrices corresponding to the robot performing the target task according to the desired motion trajectory information and the motion state information; constructing matching dynamic constraints for task-driven parameters of the robot according to the desired motion trajectory information and the motion state information; constructing matching parameter distribution constraints for the task-driven parameters according to the motion state information and body action safety constraints corresponding to the target task; solving a pre-stored task execution loss function by using the task execution coefficient matrices to obtain the target-driven parameters satisfying the dynamic constraints and the parameter distribution constraints; and controlling operation state of each joint end effector of the robot according to the target-driven parameters.