Dynamic Robot Motion Planning Under Physical Constraints
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Solution Overview
Problem
Current robotic systems face challenges in rapid and efficient programming of movements, often requiring extensive hardcoding and failing to account for environmental and physical constraints, leading to bottlenecks and potential damage during execution.
Innovation Solution
A dynamic planning controller that transforms user-defined maneuvers into nonlinear optimization problems, linearizes them using quadratic programming, and generates joint commands to control legged robots, allowing for flexible and accurate movement generation with minimal user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional robotic programming methods are used, then programming can be completed with existing tools, but extensive hardcoding is required and environmental/physical constraints are not accounted for
Solution Approach 1:
The patent replaces traditional mechanical programming approaches (hardcoding movements and constraints) with a computational optimization system. The controller transforms user-defined maneuvers into nonlinear optimization problems and solves them using quadratic programming, automatically generating joint commands without extensive manual programming. This substitution of mechanical programming with computational optimization resolves the contradiction by reducing hardcoding requirements while maintaining programming capability.
2Manufacturing precision
If nonlinear optimization problems are solved directly, then accurate movement execution is achieved, but computational time increases and real-time control becomes difficult
Solution Approach 1:
The patent changes the parameters of the optimization problem by linearizing the nonlinear optimization problem into a quadratic programming problem that can be solved in real-time. The system transforms the original nonlinear constraints and objectives into a linearized form with quadratic cost function, maintaining movement accuracy while reducing computational time to enable real-time control of legged robots.
3Measurement precision
If iterative linearization is performed multiple times, then solution accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent implements feedback through iterative linearization where each iteration uses the solution from the previous iteration to refine the linearization. The controller performs multiple iterations of linearizing the nonlinear optimization problem, with each iteration improving solution accuracy. The feedback loop allows the system to converge to an accurate solution while managing computational complexity through efficient use of previous iteration results.
Data Source
AI summary
A dynamic planning controller receives a maneuver for a robot and a current state of the robot and transforms the maneuver and the current state of the robot into a nonlinear optimization problem. The nonlinear optimization problem is configured to optimize an unknown force and an unknown position vector. At a first time instance, the controller linearizes the nonlinear optimization problem into a first linear optimization problem and determines a first solution to the first linear optimization problem using quadratic programming. At a second time instance, the controller linearizes the nonlinear optimization problem into a second linear optimization problem based on the first solution at the first time instance and determines a second solution to the second linear optimization problem based on the first solution using the quadratic programming. The controller also generates a joint command to control motion of the robot during the maneuver based on the second solution.