Robot Leg Swing Trajectory Planning for Obstacle-Aware Foot Placement
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Solution Overview
Problem
Robotic devices face challenges in navigating constrained environments efficiently without colliding with obstacles while maintaining balance and speed, as existing navigation methods are slow and often result in collisions or loss of balance.
Innovation Solution
A method for planning leg swing trajectories in real-time by separating motion into horizontal and vertical components, selecting policies based on aggressive vertical motion and tier-based horizontal policies to optimize trajectory planning, and executing these policies to ensure efficient and balanced navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time navigation and step planning is implemented to avoid obstacles, then collision avoidance capability is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the leg swing trajectory planning into independent horizontal and vertical motion components. Each component has its own policy set that can be evaluated and selected separately, reducing the computational complexity of evaluating combined trajectories while maintaining the ability to plan collision-free paths in real-time.
Solution Approach 2:
The patent implements dynamic policy selection where multiple horizontal and vertical motion policies are evaluated based on current robot state and obstacle positions. The system dynamically selects the most appropriate policy combination for each step, enabling adaptive real-time navigation without requiring pre-computed fixed trajectories.
2Speed
If aggressive vertical motion policies are used to maximize swing speed, then navigation speed is improved, but stability and balance control become more difficult
Solution Approach 1:
The patent separates vertical motion control from horizontal motion control, allowing independent optimization of each component. Aggressive vertical policies can be selected to maximize swing speed while horizontal policies are selected separately to ensure stable, collision-free positioning, resolving the trade-off between speed and stability.
Solution Approach 2:
The system changes motion parameters dynamically by selecting from multiple vertical motion policies with different aggressiveness levels. Based on the situation, the system can choose more conservative or more aggressive vertical motion profiles, allowing flexible adjustment of the speed-stability trade-off.
3Productivity
If multiple motion policies are evaluated and selected based on tiers and tiebreaking parameters, then trajectory optimization is improved, but processing time increases
Solution Approach 1:
The patent divides the policy evaluation process into separate horizontal and vertical components, each with its own tier system. This segmentation allows the system to evaluate and select policies independently for each dimension, reducing the total number of combinations that must be evaluated compared to evaluating all possible combined trajectories.
Solution Approach 2:
The system uses tier-based evaluation where policies are ranked and only the top-tier policies need to be considered for final selection. The tiebreaking parameters provide a systematic way to resolve ambiguities without requiring exhaustive evaluation of all policy combinations, achieving good enough solutions faster than complete optimization.
Data Source
AI summary
A method of planning a swing trajectory for a leg of a robot includes receiving an initial position of a leg of the robot, an initial velocity of the leg, a touchdown location, and a touchdown target time. The method also includes determining a difference between the initial position and the touchdown location and separating the difference between the initial position and the touchdown location into a horizontal motion component and a vertical motion component. The method also includes selecting a horizontal motion policy and a vertical motion policy to satisfy the motion components. Each policy produces a respective trajectory as a function of the initial position, the initial velocity, the touchdown location, and the touchdown target time. The method also includes executing the selected policies to swing the leg of the robot from the initial position to the touchdown location at the touchdown target time.