Hybrid Robot Task-Space Control for Whole-Body Stability
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Solution Overview
Problem
Existing robotic systems face challenges in integrating interaction control with whole-body posture control, particularly in mobile robots like exoskeletons and humanoid robots, leading to instability and inefficiencies due to underactuated dynamics and complex constraints.
Innovation Solution
A hybrid control framework using physics-based modeling and hierarchical quadratic programming to manage whole-body kinematics and dynamics, incorporating user input and autonomous operation, with a controller that prioritizes tasks and ensures stability through contact force control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If interaction control and whole-body posture control are integrated in mobile robots, then robot functionality and task performance are improved, but system complexity and control difficulty increase due to underactuated dynamics and complex constraints
Solution Approach 1:
The control system is segmented into multiple hierarchical layers: high-level task planning, mid-level whole-body control, and low-level joint control. Each layer handles specific aspects of control, reducing the complexity of integrating interaction control and posture control while maintaining comprehensive functionality.
Solution Approach 2:
A whole-body controller acts as an intermediary between interaction control requirements and posture control execution. This mediator coordinates the underactuated dynamics and complex constraints by transforming high-level task commands into coordinated joint movements that satisfy both interaction and posture requirements.
2Productivity
If hybrid control framework with hierarchical quadratic programming is used, then task execution efficiency and stability are improved, but computational complexity increases
Solution Approach 1:
The system pre-computes task priorities, constraints, and optimization parameters before real-time execution. By preparing the quadratic programming problem structure in advance and updating only the critical variables during operation, the system achieves efficient task execution while managing computational complexity.
Solution Approach 2:
The hierarchical quadratic programming framework dynamically adjusts control priorities and constraints based on real-time robot states and task requirements. This dynamic adaptation allows the system to maintain high task execution efficiency while optimizing computational resource allocation to manage complexity.
3Stability of the object's composition
If contact force control is implemented to ensure stability, then robot balance and safety are improved, but control precision requirements increase
Solution Approach 1:
The contact force control system continuously monitors robot states, contact forces, and environmental interactions, using this feedback to dynamically adjust control commands. This closed-loop feedback mechanism maintains robot balance and safety while compensating for uncertainties, reducing the stringency of control precision requirements.
Data Source
AI summary
Technology is described for a controller or control service that facilitates the use of robots as hybrid machines. More specifically, a task space controller can be provided for an robot with hybrid input from either a human or programmed tasks.