Robot Motion Control Using State-Environment Parameter Fusion
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Solution Overview
Problem
Existing robot control methods using fixed program instructions result in low motion performance due to inadequate adaptation to the environment.
Innovation Solution
A robot control method that acquires state and environmental data, predicts initial and environmental impact parameters, and fuses them to generate a control instruction for improved adaptation and simulation of target object actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed program instructions are used for robot control, then the control system is simple and easy to implement, but the robot's motion performance and environmental adaptation are poor
Solution Approach 1:
The patent implements dynamic control parameters that adapt to environmental conditions. The robot uses environmental sensing to detect terrain characteristics and dynamically adjusts control parameters such as step length, step frequency, and body posture to optimize motion performance for different environments including flat ground, slopes, and uneven terrain.
Solution Approach 2:
The system changes control parameters based on environmental feedback. By monitoring environmental data and motion state, the robot modifies parameters like joint angles, motor speeds, and force application to adapt to varying terrain conditions, transforming a static control system into one that responds dynamically to environmental changes.
2Ease of manufacture
If fixed program instructions are used for robot control, then the programming is straightforward, but the robot cannot accurately adapt to its environment
Solution Approach 1:
The patent implements a feedback mechanism where environmental sensors continuously monitor terrain conditions and motion state data is collected from onboard sensors. This feedback loop allows the robot to detect deviations from desired motion and automatically adjust control parameters to maintain accurate motion performance across varying environmental conditions.
Solution Approach 2:
The robot performs self-adjustment of control parameters based on environmental feedback without requiring external reprogramming. The system autonomously modifies its control strategy by processing environmental data and motion state information, enabling accurate adaptation to different terrains while maintaining straightforward initial programming.
3Adaptability or versatility
If environmental adaptation is improved through dynamic parameters, then motion performance increases, but the control system complexity increases
Solution Approach 1:
The patent segments the control system into modular functional components: environmental sensing modules, motion state detection modules, parameter prediction modules, and control execution modules. Each module performs a specific function, and they are interconnected through standardized interfaces, reducing overall system complexity while enabling sophisticated environmental adaptation.
Solution Approach 2:
The patent introduces intermediary processing layers between environmental sensing and control execution. Prediction modules serve as intermediaries that process raw environmental data and motion state information to generate optimized control parameters, simplifying the overall control architecture by separating data processing from control decision-making.
Data Source
AI summary
Provided are a robot control method and apparatus, an electronic device, a storage medium, and a program product. The method includes: acquiring state data configured for indicating a current motion state of a robot, and acquiring environmental data configured for indicating an environment where the robot is currently located; predicting, based on the state data, an initial action parameter configured for controlling the robot to imitate an object action of a target object; predicting, based on the environmental data, an environmental impact parameter configured for representing impact generated by the environment on imitation of the object action by the robot; fusing the initial action parameter and the environmental impact parameter to obtain a fused action parameter of the robot; and generating a control instruction based on the fused action parameter, the control instruction being configured for controlling the robot to perform a target action indicated by the fused action parameter.