Omnidirectional Humanoid Robot Control for High-Speed Stability
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Solution Overview
Problem
Current humanoid robots lack the ability to autonomously adapt their behavior to dynamic environments without programmer intervention, struggling with dynamic stability and motion control due to their complex kinematics and high center of pressure constraints.
Innovation Solution
A humanoid robot with an omnidirectional mobile base and a double point-mass model for dynamic control, using linear model predictive control with quadratic optimization to manage center of pressure, velocity, and acceleration, allowing for high-speed motion while maintaining stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the robot uses a mobile base with wheels to achieve high-speed motion, then the speed increases, but the mobile base suffers from strong slippages and stability deteriorates
Solution Approach 1:
The control method dynamically adjusts control parameters (position, velocity, acceleration commands) based on real-time feedback from position sensors and a dynamic model of the robot, adapting to changing conditions to maintain stability during high-speed motion
Solution Approach 2:
The system uses position sensors to continuously measure the robot's position and implements a closed-loop control method that processes this feedback information through a dynamic model to generate appropriate control commands, correcting deviations and preventing slippage
2Adaptability or versatility
If the robot has a high center of pressure to achieve a more human-like form, then the humanoid appearance is improved, but the robot can easily fall and dynamic stability deteriorates
Solution Approach 1:
The control method explicitly accounts for the high center of pressure in the dynamic model, using real-time position and acceleration data to calculate control commands that actively compensate for the instability caused by the elevated center of pressure, enabling the robot to maintain balance during motion
Solution Approach 2:
The system uses a dynamic model to predict future states and calculate control commands in advance, ensuring that the center of pressure remains within the support polygon before instability occurs, preventing falls proactively
3Stability of the object's composition
If the robot uses traditional control methods to maintain stability, then the robot remains stable, but the motion velocity and acceleration are limited
Solution Approach 1:
The control method optimizes control parameters (position, velocity, acceleration commands) by processing feedback through a dynamic model that explicitly accounts for stability constraints, enabling the robot to operate at the boundaries of stability and achieve higher speeds and accelerations than traditional conservative methods
Solution Approach 2:
By using a dynamic model to predict future states and calculate optimal control commands in advance, the system can plan trajectories that maximize speed and acceleration while ensuring stability constraints are satisfied throughout the motion sequence
4Adaptability or versatility
If the robot implements autonomous behavior without programmer intervention, then the adaptability improves, but the control system complexity increases
Solution Approach 1:
The robot determines its own behavior sequences autonomously by processing sensor feedback through a dynamic model and generating control commands without external programmer intervention, making the system self-adapting to environmental conditions
Solution Approach 2:
The control system is organized as a modular feedback loop with distinct components: position sensing, dynamic model processing, and control command generation, which manages complexity by separating functions while enabling autonomous operation
Data Source
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AI summary
The object of the invention is a humanoid robot (100) with a body (190) joined to an omnidirectional mobile ground base (140), and equipped with: - a body position sensor and a base position sensor to provide measures, - actuators (212) comprising at least 3 wheels (141) located in the omnidirectional mobile base, - extractors (211) for converting the measures into useful data, - a controller to calculate position, velocity and acceleration commands from the useful data using a robot model and preordered position and velocity references, - means for converting the commands into instructions for the actuators, characterized in that the robot model is a double point-mass model, and in that the commands are based on a linear model predictive control law with a discretized time according to a sampling time period and a number of predicted samples, and expressed as a quadratic optimization formulation with: - a weighted sum of objectives - a set of predefined linear constraints