Robot Motion Planning Controller Using Iterative Quadratic Programming
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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 to the robot or its environment.
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 robot motion, allowing for flexible and accurate movement generation with minimal user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional hardcoding methods are used to program robot movements, then movement precision can be controlled, but programming time and complexity increase significantly
Solution Approach 1:
The patent replaces traditional mechanical programming approaches (hardcoding) with a computational optimization system. The motion planning is transformed into a nonlinear optimization problem that automatically generates trajectories by optimizing objective functions, substituting manual programming mechanics with automated mathematical optimization.
Solution Approach 2:
The system changes the fundamental parameters of motion planning by introducing cost functions and optimization variables. Instead of hardcoding specific movement sequences, the system defines optimization parameters (cost functions, constraints, weights) that automatically generate appropriate trajectories, allowing rapid adaptation by simply changing parameter values rather than rewriting code.
2Manufacturing precision
If detailed movement programming is specified to ensure accurate execution, then movement accuracy improves, but system complexity and difficulty of operation increase
Solution Approach 1:
The optimization system performs self-service by automatically generating motion trajectories without requiring detailed manual programming. The cost functions and constraints define the desired behavior, and the optimization algorithm autonomously computes the specific trajectories, eliminating the need for operators to manually program detailed movement sequences while maintaining high accuracy.
3Device complexity
If traditional control methods are used without considering environmental constraints, then control simplicity is maintained, but robot or environment damage may occur
Solution Approach 1:
The system performs preliminary action by incorporating environmental and physical constraints into the optimization problem before generating trajectories. Contact forces, friction constraints, and environmental boundaries are pre-defined in the optimization formulation, ensuring that generated trajectories inherently satisfy safety requirements before execution, preventing potential damage rather than reacting to it.
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.