Robotic Finishing Setup Planning for Full Surface Coverage
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Solution Overview
Problem
Robotic finishing tasks, such as polishing and sanding, require multiple setups to access complex parts, leading to increased time and complexity due to limited workspace and collision avoidance challenges, necessitating efficient setup planning and trajectory management.
Innovation Solution
A method for automated setup planning that involves sampling multiple poses in a robotic workspace to generate candidate configurations, determining scores based on area coverage and setup time, and optimizing these configurations to minimize setup changes and collisions, using processors and actuators to control the robot's position and orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple setups are used to access complex parts, then the robot can cover the entire region of interest, but the setup time and complexity increase
Solution Approach 1:
The system performs preliminary sampling of multiple poses and generates candidate configurations before actual finishing operations. By pre-calculating and scoring potential setups based on area coverage and setup time, the system identifies optimal configurations in advance, reducing actual setup time during execution.
Solution Approach 2:
The system dynamically adjusts the number and positioning of setups based on real-time scoring of candidate configurations. Rather than using a fixed number of setups, the system optimizes the setup sequence by evaluating area coverage and setup time for each candidate, adapting the planning to minimize total setup time while ensuring complete region coverage.
2Manufacturing precision
If the robot manipulates the tool following a trajectory, then finishing precision is improved, but collision risks and operational complexity increase
Solution Approach 1:
The system incorporates feedback mechanisms by scoring candidate configurations based on multiple criteria including area coverage and setup time. This scoring system provides feedback that guides the selection of optimal trajectories and joint configurations, balancing precision requirements with collision avoidance and operational efficiency.
Solution Approach 2:
The system changes multiple parameters simultaneously when optimizing trajectories, including joint configurations, tool positions, and motion speeds. By coordinating these parameter changes across multiple dimensions, the system achieves precise finishing while managing complexity through integrated optimization rather than sequential adjustment.
3Adaptability or versatility
If the robot repositions the part for better access, then workspace limitations are overcome, but the number of setup changes increases
Solution Approach 1:
The system performs preliminary sampling of multiple poses to generate candidate configurations that include various part repositioning options. By evaluating these candidates in advance based on area coverage and setup time, the system determines the minimum necessary number of setup changes required to access the entire region of interest.
Solution Approach 2:
The system samples poses in six-dimensional space, considering both three-dimensional position and three-dimensional orientation of the part. This multi-dimensional approach allows the system to find optimal repositioning strategies that minimize setup changes while ensuring complete accessibility to the region of interest from multiple angular perspectives.
Data Source
AI summary
Methods, systems, and platforms for automatic setup planning for a robot. The method includes sampling multiple poses in multiple dimensions within a robotic workspace. The method includes generating one or more candidate configurations based on the multiple poses. The method includes determining a score for each candidate configuration of the one or more candidate configurations. The score represents area coverage of a region of interest and at least one of an amount of setup time of the candidate configuration or an amount of energy used. The method includes determining a set of candidate configurations that has an overall area coverage that covers the region of interest based on the score for each candidate configuration. The method includes controlling a position and an orientation of the object based on the set of candidate configurations.