Redundant Robot Control with Passive Weighted Task Prioritization
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Solution Overview
Problem
Current methods for controlling kinematically redundant robots face challenges in simultaneously managing multiple tasks with strict task hierarchies, robustness against singularities, model errors, and sensor noise, while also ensuring passivity and flexibility in implementation.
Innovation Solution
A Modular Passive Tracking Controller (MPTC) is developed, combining passivity-based tracking controller modules through optimization with weighted task prioritization, using natural robot inertia and compensating Coriolis and centrifugal effects to achieve a spring-mass-damper behavior, ensuring passivity and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If hierarchical controllers based on null-space projections are used to enforce strict task priorities, then task hierarchy is guaranteed, but the system loses robustness against task singularities and requires complex singularity-robust methods that ultimately create task weighting
Solution Approach 1:
The control system is segmented into multiple independent passive tracking controller modules, each responsible for a specific task. These modules are combined through optimization with weighted task prioritization, allowing the system to maintain task hierarchy while avoiding the singularity problems associated with null-space projection methods.
Solution Approach 2:
The patent employs dynamic task prioritization through weighted combination of controller modules rather than static hierarchical structures. This dynamic approach allows the system to adapt to singularities and model errors by adjusting task weights, maintaining robustness while preserving task hierarchy.
2Ease of operation
If inverse dynamics-based tracking controllers are used to achieve smooth compromise between multiple tasks, then implementation flexibility and ease of use are improved, but robustness against model errors and contact uncertainties deteriorates, causing vibrations
Solution Approach 1:
The patent uses simplified passive tracking controller modules that are computationally efficient and easy to implement, similar to 'cheap' solutions. However, these simple modules are combined through optimization to achieve the robustness of more complex methods, getting the benefits of both simplicity and reliability.
Solution Approach 2:
The control system combines multiple passive tracking controller modules into a composite control structure. Each individual module is simple and easy to implement, but their weighted combination through optimization creates a robust system that withstands model errors and contact uncertainties.
3Reliability
If passivity-based controller modules are combined through optimization with weighted task prioritization, then robustness and passivity are maintained, but the device complexity increases compared to simple inverse dynamics methods
Solution Approach 1:
The complex control problem is segmented into multiple independent passive tracking controller modules, each handling a specific task. This segmentation makes the overall complex system manageable by breaking it down into simpler, independent components that can be individually designed and tuned.
Solution Approach 2:
The passive tracking controller modules are designed to be universal and multi-functional, capable of handling different tasks through weighted combination. This universality reduces the need for task-specific complex controllers, as the same modular structure can adapt to various control scenarios.
4Stability of the object's composition
If strict task decoupling is enforced to ensure independent transient response, then task independence is achieved, but the ability to handle kinematically redundant robots performing multiple simultaneous tasks is limited
Solution Approach 1:
The system employs dynamic task prioritization through weighted combination of controller modules, allowing tasks to be coupled or decoupled based on current operational needs. This dynamic approach enables the system to maintain task independence when needed while allowing interaction and coordination for redundant degree of freedom exploitation.
Solution Approach 2:
The task weights in the optimized combination of controller modules can be dynamically adjusted to change the degree of task coupling. By modifying these parameters, the system can transition between strict task independence and coordinated multi-task execution, adapting to different operational requirements.
Data Source
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AI summary
The invention relates to a method for controlling a kinematically redundant robot (100) in order to fulfill multiple tasks, wherein at least one passivity-based first controller module (102) is used, at least one task target description and at least one corresponding task mapping are calculated for the at least one first controller module (102), at least one weighting is calculated for the tasks, and the at least one first controller module (102) is integrated into a complete controller (104) using the at least one weighting. The invention also relates to a computer program product comprising commands which cause at least one processor to carry out such a method when running the program using said processor.