Robot Hierarchical Task Control Using Null-Space Projection
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Solution Overview
Problem
Existing robot control methods face challenges in managing multiple tasks simultaneously, particularly when a robot is in a singular position, as they often result in mutual interference between control signals, and require cumbersome re-determination of controllers and dynamics decomposition when tasks change.
Innovation Solution
A task hierarchical control method that divides tasks into subtasks using selection matrices and prioritizes their execution, employing a controller library for efficient task management, and utilizes null space projection matrices to prevent interference, allowing for simultaneous execution of multiple tasks without re-decomposition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct superposition method is used to combine control signals for multiple tasks, then the robot can perform multiple tasks simultaneously, but mutual interference between controls of different tasks occurs
Solution Approach 1:
The control space is segmented into task-specific subspaces using selection matrices. Each task is assigned a specific subspace through selection matrices, allowing independent control within each subspace. This segmentation prevents mutual interference between tasks by ensuring that control signals for different tasks operate in orthogonal subspaces, thereby maintaining reliability while enabling multi-task execution.
Solution Approach 2:
Selection matrices and null space projection matrices serve as intermediaries between the control inputs and the robot's motion. These matrices transform the control signals in a way that automatically satisfies task priorities and eliminates interference. The intermediary transformation ensures that higher-priority tasks are executed without being affected by lower-priority tasks, resolving the interference problem while maintaining productivity.
2Adaptability or versatility
If controllers and control methods are re-determined when tasks change, then task adaptability is achieved, but the overall process becomes cumbersome
Solution Approach 1:
The control framework uses universal selection matrices and null space projection matrices that can handle any task configuration. Instead of determining new controllers for each task change, the same universal framework adapts to different tasks by simply changing the selection matrices. This multi-functional approach allows the system to maintain adaptability while avoiding the time-consuming process of re-determining controllers, as the underlying control structure remains consistent across different tasks.
3Manufacturing precision
If dynamics decomposition is performed for each task, then precise task control is achieved, but the control process becomes complex and time-consuming
Solution Approach 1:
The control problem is transformed from the joint space to the task space using selection matrices and null space projection. By working in the task space dimension, the control precision is maintained for each specific task while the overall control process becomes simpler. The dimensionality change allows precise control to be achieved without performing complex dynamics decomposition for each task, as the selection matrices automatically handle the task-specific control requirements in a unified framework.
Data Source
AI summary
A task hierarchical control method as well as a robot and a storage medium using the same are provided. The method includes: obtaining a task instruction for a robot, where the task instruction is for determining a target task card including an amount of selection matrices for dividing a target task into the amount of hierarchical subtasks and a controller name for executing each of the hierarchical subtasks; obtaining a null space projection matrix of each of the hierarchical subtasks based on the corresponding selection matrix; generating control finks of the amount according to the corresponding controller of each of the hierarchical subtasks and the corresponding null space projection matrix; calculating a control torque of each of the control links and obtaining a hierarchical control output quantity by adding ail the control torques; and controlling the robot to perform the target task using the hierarchical control output quantity.