Robotic Step Path Selection Using Capture Point Stability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Legged robots often face challenges in determining feasible step paths that maintain stability and adhere to operational constraints, particularly when their legs are required to step beyond their range-of-motion or risk falling over.
Innovation Solution
A method for legged robots to evaluate potential step paths by calculating error values based on similarity to a reference path and stability criteria, using a combined error value to select a suitable path that balances deviation and stability, and dynamically updating paths in response to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the robot steps beyond its range-of-motion to follow a reference path, then the robot can achieve the desired trajectory, but the robot risks falling over and loses stability
Solution Approach 1:
The system dynamically adjusts the step path by evaluating multiple potential paths and selecting one that maintains stability. The capture point trajectory is continuously updated based on the robot's current state, allowing the robot to adapt its movement in real-time to avoid instability while still progressing toward the target trajectory.
Solution Approach 2:
The system changes the parameters of the step path by calculating error values between the reference capture point trajectory and potential step paths. By minimizing this error, the system finds optimal step paths that balance trajectory accuracy with stability constraints, effectively adjusting movement parameters to resolve the contradiction.
2Manufacturing precision
If the robot selects a step path that closely matches the reference path, then the robot achieves high trajectory accuracy, but the robot may violate operational constraints such as range-of-motion limits
Solution Approach 1:
The system uses feedback by calculating error values that compare the reference capture point trajectory with potential step paths. This feedback mechanism allows the robot to evaluate whether a path maintains constraint compliance while achieving trajectory accuracy, and to select paths that satisfy both requirements.
Solution Approach 2:
The step path selection is dynamic and adaptive, evaluating multiple potential paths against both accuracy and constraint compliance criteria. The system can switch between different paths based on real-time conditions, ensuring constraint compliance while maintaining high trajectory accuracy when possible.
3Stability of the object's composition
If the robot evaluates multiple potential step paths to maintain stability, then the robot achieves better stability, but the computational complexity and time required increases
Solution Approach 1:
The system performs self-service by automatically evaluating and selecting step paths based on pre-defined error metrics and stability criteria. This automated process reduces the need for complex external control interventions while maintaining stability through systematic path evaluation.
Solution Approach 2:
The system manages complexity by changing parameters in a structured way - calculating error values between reference and potential paths using consistent mathematical relationships. This parameter-based approach provides a systematic method for evaluating multiple paths without requiring overly complex control logic.
4Adaptability or versatility
If the robot dynamically updates step paths in response to environmental changes, then the robot maintains adaptability, but the computational load and processing time increases
Solution Approach 1:
The system dynamically updates step paths by continuously evaluating potential paths against the reference trajectory and stability criteria. This dynamic approach allows the robot to adapt to environmental changes while using efficient error calculation methods to minimize processing time.
Solution Approach 2:
The system performs preliminary evaluation of potential step paths using pre-defined error metrics and capture point trajectory relationships. By having evaluation criteria ready in advance, the system can quickly assess new paths when environmental changes occur, reducing processing time while maintaining adaptability.
Data Source
AI summary
A method of robotic stepping includes determining a first step location error between a reference step location of a reference step path and a first potential step location of a first potential step path for a first leg of a robot, determining a first capture point error between a reference capture point location of the reference step path and a first potential capture point location of the first potential step path, determining a first score for the first potential step path based on the first step location error and the first capture point error, selecting the first potential step path based on comparing the first score for the first potential step path to a second score of a second potential step path, and instructing a movement of the first leg of the robot based on the first potential step path.