Robot Motion Optimization via Virtual Simulation
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Solution Overview
Problem
Robotic motion planning tools fail to optimize robotic movements based on variations in object weight and material, leading to inefficiencies and potential errors in manufacturing processes, as they do not consider factors like power efficiency and accuracy.
Innovation Solution
A system that includes a motion planner and optimizer, which generates and refines motion scripts for robots based on performance criteria such as accuracy, speed, and power efficiency, using virtual simulations to optimize real-world robotic operations, allowing for precise positioning and reduced errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If robot control planning software provides same movements based upon operation being performed, then implementation simplicity is maintained, but manufacturing precision and power efficiency deteriorate due to lack of optimization based on object variations
Solution Approach 1:
The system changes motion parameters (velocity, acceleration, trajectory) based on object parameters (weight, dimensions, material properties). The motion planner adjusts motion scripts dynamically according to the specific object being manipulated, transforming the robot's motion characteristics to optimize for each object's unique properties rather than using fixed motion patterns
Solution Approach 2:
The system performs preliminary simulation and optimization of motion scripts in a virtual environment before executing real robotic operations. The motion planner generates multiple candidate motion scripts, the simulator evaluates them virtually, and the optimal script is selected before actual robot execution, allowing optimization to be done in advance without affecting production time
2Manufacturing precision
If multiple motion scripts are generated and simulated for optimization, then manufacturing precision and power efficiency are improved, but computational time and system complexity increase
Solution Approach 1:
The system creates a virtual copy (digital twin) of the robotic cell including the robot, object, and environment. Motion scripts are simulated and evaluated in this virtual copy rather than testing multiple physical configurations. The virtual simulator replicates physical behaviors and constraints, allowing comprehensive optimization without additional physical hardware complexity
Solution Approach 2:
The optimization system is divided into distinct modular components: motion planner (generates scripts), simulator (evaluates scripts), and optimizer (selects optimal script). Each component performs a specific function independently, making the overall complex system manageable through functional segmentation and allowing parallel processing of multiple motion script evaluations
3Measurement precision
If motion scripts are optimized in virtual environment before real execution, then positioning accuracy and safety are improved, but processing time for script generation increases
Solution Approach 1:
The system performs all positioning parameter alignment and motion optimization in the virtual environment before real robot execution. The simulator pre-calculates optimal trajectories and parameters, and the motion planner generates refined scripts in advance. This preliminary optimization eliminates the need for time-consuming real-world trial-and-error adjustments
Solution Approach 2:
The simulator provides feedback on motion script performance by evaluating positioning accuracy, collision risks, and energy consumption. This feedback loop allows the optimizer to iteratively refine motion scripts until optimal performance is achieved, with the best script then executed in the real robotic cell without requiring further adjustments
Data Source
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AI summary
A trajectory optimization system and method for robot cells includes cameras, robots, operated objects, a robot planner, a motion optimizer and a robot controller. The motion planner produces a plurality of motion scripts for an assembly scenario. The motion optimizer runs each of the plurality of motion scripts on a virtual, simulated robotic environment and determines the optimum motion script based upon criteria values associated with robotic performance. If errors are detected, the motion optimizer can correct the assembly scenario and request a revised list of motion scripts. The error free optimized motion script is run by the robot controller which generates control signals for the real robot environment.