Robot Motion Control With Synchronous Pose, Speed, and Acceleration
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Solution Overview
Problem
Conventional motion control approaches for robots primarily focus on position (or pose) adjustment during motion, leading to limited accuracy and efficiency as acceleration adjustment is secondary, resulting in suboptimal robot trajectory performance.
Innovation Solution
A system that synchronously adjusts pose, acceleration, and speed using a motion parameter determination model, such as a deep deterministic policy gradient (DDPG) model, to improve motion accuracy and efficiency by optimizing motion parameters at each trajectory point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional motion control approaches focus primarily on position adjustment during motion, then the control system is simpler to implement, but the robot trajectory accuracy and motion efficiency are limited
Solution Approach 1:
The patent combines position, acceleration, and speed adjustments into a unified motion parameter determination model. The DDPG model simultaneously determines multiple motion parameters (position, acceleration, speed) for each trajectory point, rather than handling them separately. This merging of control functions into a single integrated system resolves the contradiction by achieving high trajectory accuracy through comprehensive parameter optimization while managing complexity through a unified neural network architecture that processes all parameters together.
Solution Approach 2:
The patent changes the control approach from primary position adjustment with secondary acceleration correction to synchronous adjustment of position, acceleration, and speed parameters. The motion parameter determination model outputs multiple parameters simultaneously, and the system dynamically adjusts the weighting of each parameter during training and execution. This parameter change enables high-precision trajectory control by optimizing all motion parameters together, resolving the accuracy-complexity contradiction.
2Productivity
If acceleration adjustment is performed secondarily after position adjustment, then the control process is simpler, but the robot motion efficiency and trajectory performance are suboptimal
Solution Approach 1:
The patent merges position control and acceleration control into a single synchronous adjustment process. The motion parameter determination model simultaneously determines optimal values for both position and acceleration at each trajectory point, eliminating the sequential two-stage approach. This integration improves motion efficiency by optimizing acceleration and position together, while the unified neural network model manages the increased complexity through joint training on multiple parameters.
Solution Approach 2:
The patent performs preliminary determination of acceleration parameters alongside position parameters before actual motion execution. The DDPG model predicts optimal acceleration values in advance as part of the motion planning process, allowing the robot to prepare for upcoming trajectory changes more effectively. This preliminary action improves motion efficiency by avoiding reactive adjustments, while the complexity is managed through the predictive nature of the neural network model.
3Measurement precision
If only position parameters are adjusted during robot motion, then the control system is easier to operate, but the motion accuracy and task completion precision are limited
Solution Approach 1:
The patent implements self-service through the motion parameter determination model, which automatically determines optimal values for position, acceleration, and speed parameters without requiring manual intervention. The DDPG model is trained offline to learn the optimal control policy, and during operation, it autonomously outputs adjusted motion parameters based on the current trajectory point and task requirements. This self-service capability achieves high motion parameter accuracy while maintaining ease of operation, as the system handles the complexity of multi-parameter adjustment automatically without increasing the operational burden on users.
4Reliability
If multiple motion parameters (pose, acceleration, speed) are adjusted synchronously using a motion parameter determination model, then the robot motion accuracy and efficiency are improved, but the computational complexity and model training requirements increase
Solution Approach 1:
The patent changes the control approach from separate parameter control to synchronous multi-parameter adjustment using the DDPG model. The model is trained to output multiple motion parameters simultaneously, and the system dynamically adjusts the importance weighting of each parameter during training and execution. This parameter change improves reliability by optimizing all motion parameters together, while the complexity is managed through the unified neural network architecture that processes multiple parameters in a coordinated manner rather than independently.
Data Source
AI summary
A method and system for motion control may be provided. A target trajectory of an object to be moved may be obtained. The object may be caused to move along the target trajectory of the object based on one or more motion parameters of the object during the movement of the object. At least a portion of the one or more motion parameters of the object may be determined using a motion parameter determination model. The one or more motion parameters of the object may include at least one of a pose parameter, an acceleration parameter, or a speed parameter.