Robot Motion Planning Controller for Constraint-Aware Maneuvers
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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 inefficiencies and potential damage.
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
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 hardcoding approaches with a computational optimization system. Instead of manually programming each movement parameter, the system uses nonlinear optimization algorithms to automatically generate motion trajectories that satisfy task requirements and physical constraints, dramatically reducing programming time while maintaining precision
Solution Approach 2:
The system transforms the movement programming problem from a static hardcoding approach to a dynamic optimization process. By changing the parameters of the optimization problem (objective function, constraints, initial conditions), the same computational framework can generate different movement solutions without requiring separate hardcoding for each scenario
2Adaptability or versatility
If traditional control methods are used, then system simplicity is maintained, but ability to account for environmental and physical constraints is insufficient
Solution Approach 1:
The controller segments the complex control problem into distinct components: task specification, nonlinear optimization formulation, constraint definition, and solution execution. This modular segmentation allows the system to handle multiple types of constraints (environmental, physical, safety) by adding them as separate constraint modules without fundamentally redesigning the entire control system
Solution Approach 2:
The patent introduces an optimization-based intermediary layer between high-level task specifications and low-level motor control. This intermediary translates abstract task requirements and constraints into concrete motion trajectories and control commands, enabling the system to handle complex constraints without requiring direct complex control logic at each execution level
3Measurement precision
If iterative optimization is performed at multiple time instances, then solution accuracy improves, but computational time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining the optimization framework, constraint structures, and objective functions before actual movement execution. This preliminary setup allows the iterative optimization to focus only on solving for specific trajectory parameters rather than reconstructing the entire optimization problem, reducing computational time while maintaining accuracy
Solution Approach 2:
The patent implements periodic optimization updates at strategically selected time instances during movement execution. Rather than continuous optimization, the system periodically re-solves the optimization problem at key moments (e.g., at movement milestones or when constraints change), balancing solution accuracy with computational efficiency
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.