Robotic Arm Control with Target-Relative End Effector Positioning
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Solution Overview
Problem
Kinesthetic demonstration in robots results in movement and positional errors due to robot body movement, obstacles, and changes in target object position, leading to task failures when replicating taught trajectories autonomously.
Innovation Solution
A method for controlling robotic arms that includes identifying the target object, determining the mechanical arm's position relative to the object, and using trajectory parameters and posture adjustments to ensure precise operation, combined with gravity compensation and probabilistic learning for improved trajectory prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the robot directly replicates the recorded trajectory from kinesthetic demonstration, then the operation process is simple, but the task fails due to movement errors and positional deviations
Solution Approach 1:
The patent implements feedback by detecting the actual position of the end effector during trajectory execution and comparing it with the recorded trajectory. When deviations are detected (exceeding threshold values), the system automatically adjusts the trajectory or re-executes the operation to ensure task completion success.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating multiple possible trajectories based on the recorded trajectory and adjusting parameters such as speed, acceleration, and positioning. The system prepares alternative paths in advance to handle potential deviations and ensure task success without requiring complex real-time decision-making.
2Manufacturing precision
If the robot adjusts the initial pose of the end effector to account for position errors, then the task execution accuracy improves, but the operation complexity increases
Solution Approach 1:
The patent applies parameter changes by modifying trajectory parameters (speed, acceleration, positioning) based on detected position errors. The system adjusts these parameters automatically to compensate for deviations between the actual and recorded trajectories, achieving accurate task execution without manual pose adjustment.
Solution Approach 2:
The robot performs self-service by automatically detecting its own position deviations and adjusting its trajectory without external intervention. The system uses its sensors and control algorithms to self-correct positioning errors, eliminating the need for manual pose adjustment by the operator.
3Adaptability or versatility
If the robot uses probabilistic learning to predict trajectories, then the adaptability to environmental changes improves, but the computational complexity increases
Solution Approach 1:
The patent replaces complex mechanical adjustment systems with computational methods. Instead of physically adjusting the robot to account for uncertainties, the system uses probabilistic learning algorithms to predict and compensate for trajectory deviations through software-based trajectory optimization.
Solution Approach 2:
The patent applies partial action by using probabilistic learning to predict only the critical segments of the trajectory where deviations are most likely to occur, rather than computing entire trajectory adjustments. This selective approach reduces computational complexity while maintaining adaptability to environmental changes.
Data Source
AI summary
A robotic arm control method, a robot and it's controller are provided, the method includes: receiving an instruction to identify a target object to be operated; responsive to that the fixed portion is beyond a first location area, controlling the robot to move, until a fixed portion of a first mechanical arm of the robot is within the first location area; responsive to that the fixed portion is within the first location area, receiving a control instruction for operating the target object, invoking trajectory parameters of an end effector of the first mechanical arm relative to the target object, and obtaining a target posture of the end effector relative to the target object; controlling the end effector to reach and operate the target object directly or through a tool according to the trajectory parameters and the target posture.