Legged Robot Behavior Control for Stability
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Solution Overview
Problem
Existing legged mobile robots face instability when performing tasks due to prioritization of arm motion over leg motion, leading to inappropriate body positioning and posture, which disrupts stable gaits.
Innovation Solution
A behavior control system that generates time-series patterns of state variables using a stochastic transition model to control both arm and body motions, ensuring stability by allowing flexibility in following specified motion trajectories and incorporating external forces during interactions with objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot prioritizes arm motion to carry out tasks, then task execution capability is improved, but body stability and gait continuity deteriorate
Solution Approach 1:
The control system dynamically adjusts the priority between task execution and stability maintenance based on the robot's current state. The stochastic transition model allows the system to flexibly switch between following the specified arm motion trajectory closely and prioritizing stable gaits, rather than maintaining a fixed priority relationship. This dynamic adjustment resolves the contradiction by making the priority structure adaptive to real-time conditions.
Solution Approach 2:
The system changes the control parameters stochastically based on the current state variables. When the robot is in a stable state, the control parameters allow closer following of the specified arm trajectory. When stability is compromised, the parameters shift to prioritize gait stability. This parameter adaptation enables the system to resolve the contradiction between task execution and stability maintenance.
2Measurement precision
If the robot closely follows the specified arm motion trajectory, then task execution precision is improved, but body position and posture appropriateness deteriorate
Solution Approach 1:
The system applies partial following of the specified arm motion trajectory rather than complete adherence. The stochastic transition model determines the degree of following based on current stability conditions, allowing the robot to achieve sufficient task execution precision without completely compromising body position appropriateness. This partial action approach resolves the contradiction by finding an optimal balance point.
Solution Approach 2:
The control system continuously monitors body position and posture as feedback variables and adjusts the arm motion following degree accordingly. When feedback indicates inappropriate body positioning, the system reduces the priority of trajectory following. This closed-loop feedback mechanism resolves the contradiction by dynamically balancing precision and appropriateness based on real-time system state.
3Stability of the object's composition
If the robot maintains continuous stability, then gait continuity is improved, but task execution flexibility deteriorates
Solution Approach 1:
The system dynamically adjusts the balance between gait continuity and task execution flexibility through the stochastic transition model. Rather than maintaining a fixed stability constraint, the model allows the system to adaptively modulate stability requirements based on task demands and current state, resolving the contradiction by making the stability constraint dynamic rather than static.
Solution Approach 2:
The control parameters governing stability maintenance are changed stochastically based on task execution requirements. When task flexibility is needed, the system temporarily relaxes stability constraints within acceptable limits. This parameter adaptation enables the system to resolve the contradiction between continuous stability and task flexibility.
Data Source
AI summary
A robot and a behavior control system for the same are capable of ensuring continued stability while carrying out a specified task by a motion of a body of the robot. Time-series changing patterns of first state variables indicating a motional state of an arm are generated according to a stochastic transition model such that at least one of the first state variables follows a first specified motion trajectory for causing the robot to carry out a specified task. Similarly, time-series changing patterns of second state variables indicating a motional state of the body are generated according to the stochastic transition model such that the second state variables satisfy a continuously stable dynamic condition.