Robot Joint Trajectory Planning for Dynamic Target Tracking Lag
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Solution Overview
Problem
Existing motion planning methods for robots face challenges in tracking dynamic targets due to delays in visual feedback frequency, leading to significant lag errors in online planning trajectories.
Innovation Solution
A dynamic target tracking method that predicts the motion of a target in real-time, compensates for lag in space, and performs online trajectory optimization using model prediction to reduce lag and ensure smooth motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If online trajectory planning is used to track dynamic targets, then the robot can react to changes in the environment and moving targets, but significant lag error occurs due to delay in visual feedback frequency
Solution Approach 1:
The system performs motion prediction in advance to forecast the dynamic target's future position based on current motion state. This preliminary action compensates for the inherent delay in visual feedback, allowing the robot to plan trajectories toward predicted rather than historical target positions, thereby reducing lag error while maintaining adaptability to environmental changes
2Measurement precision
If visual feedback frequency is increased to reduce lag error, then trajectory accuracy improves, but system complexity and computational burden increase
Solution Approach 1:
The system introduces a motion prediction module as an intermediary between visual feedback acquisition and trajectory planning. This mediator processes the low-frequency visual feedback to generate predicted target positions, effectively decoupling the trajectory accuracy from the visual feedback frequency. The intermediary transforms limited feedback data into accurate predictive information without requiring increased feedback frequency or system complexity
3Adaptability or versatility
If online trajectory planning is performed in real-time, then the robot can adapt to moving targets, but computational resources are insufficient for sufficiently fast solutions
Solution Approach 1:
The motion prediction module performs preliminary computation to forecast target positions before actual trajectory planning begins. By pre-calculating where the target will be rather than reacting to where it was, the system reduces the complexity and time required for real-time trajectory optimization, enabling faster computational responses while maintaining online adaptation capability
Solution Approach 2:
The system changes the temporal parameter of target position from historical feedback values to predicted future values. This parameter transformation allows trajectory planning to work with anticipatory data rather than delayed data, reducing the computational burden of correcting large lag errors while maintaining real-time adaptability to moving targets
Data Source
AI summary
A dynamic target tracking method for a robot having multiple joints includes: obtaining a motion state of a tracked dynamic target in real time; performing motion prediction according to the motion state at a current moment to obtain a predicted position of the dynamic target; performing lag compensation on the predicted position to obtain a compensated predicted position; performing on-line trajectory planning according to the compensated predicted position to obtain planning quantities of multi-step joint motion states at multiple future moments, and determining a multi-step optimization trajectory according to the planning quantities and a multi-step optimization objective function; and controlling the joints of the robot to according to the multi-step optimization trajectory.