Robot Behavior Optimization via Offline Simulation
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Solution Overview
Problem
Current robotic systems lack efficient methods to perform coordinated motions that require precise control of actuators and joints to achieve specific tasks, such as throwing objects or navigating obstacles, often resulting in suboptimal performance due to limitations in actuator constraints and lack of integrated control strategies.
Innovation Solution
A computational model is developed to simulate and optimize coordinated motions of robotic systems by determining sets of control parameters that balance actuator constraints and achieve predetermined goals, using feed-forward control inputs to modify the robot's behavior and integrate feedback control for stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If coordinated exertion of forces by multiple actuators is implemented, then task performance capability is improved, but control complexity increases
Solution Approach 1:
The control parameters are segmented into multiple subsets, where each subset corresponds to forces exerted by a specific actuator over time. This segmentation allows independent optimization and control of each actuator while maintaining coordinated action, thereby managing control complexity through modular organization of control parameters
Solution Approach 2:
The system uses a unified model-based framework that can handle multiple actuators and various task requirements through the same optimization approach. The control parameter optimization methodology is universally applicable to different robotic configurations and task types, reducing the need for task-specific control complexity
2Reliability
If actuator constraints are strictly enforced, then system reliability is improved, but task performance is degraded
Solution Approach 1:
The optimization process dynamically adjusts control parameters including actuator forces, joint movements, and timing to find optimal solutions that satisfy constraints while maximizing task performance. By changing parameters iteratively through simulation and scoring, the system achieves reliable operation within constraints while maintaining high productivity
Solution Approach 2:
The system employs dynamic optimization where control parameters are continuously adjusted based on simulated performance and constraint satisfaction. The coordinated exertion of forces is dynamically optimized over time periods, allowing the system to adaptively balance reliability requirements with task performance goals
3Measurement precision
If simulation-based optimization is performed, then control precision is improved, but computational time increases
Solution Approach 1:
The system performs preliminary simulation and optimization of control parameters before actual robot execution. By pre-optimizing the coordinated exertion of forces and joint movements through virtual simulation, the system achieves high control precision while reducing real-time computational requirements during actual task execution
Data Source
AI summary
A computing system may provide a model of a robot. The model may be configured to determine simulated motions of the robot based on sets of control parameters. The computing system may also operate the model with multiple sets of control parameters to simulate respective motions of the robot. The computing system may further determine respective scores for each respective simulated motion of the robot, wherein the respective scores are based on constraints associated with each limb of the robot and a goal. The constraints include actuator constraints and joint constraints for limbs of the robot. Additionally, the computing system may select, based on the respective scores, a set of control parameters associated with a particular score. Further, the computing system may modify a behavior of the robot based on the selected set of control parameters to perform a coordinated exertion of forces by actuators of the robot.