Robot Joint-Space Path Planning for Obstacle-Constrained Moves
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Solution Overview
Problem
Manual path planning for robot linkages in semiconductor manufacturing is time-consuming and difficult, especially in confined spaces with multiple obstacles, requiring frequent updates when the environment changes.
Innovation Solution
A system that builds a graph of reachable positions and sub-paths within joint space, using a processing device to execute a graph optimization algorithm to determine multiple paths that minimize move time while satisfying Cartesian and joint limits, allowing for automated planning and execution of safe and efficient robot movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual path planning is used for robot linkages, then the robot can avoid Cartesian limits and obstacles, but the planning process becomes time-consuming and difficult
Solution Approach 1:
The patent replaces manual mechanical path planning with an automated computational system that uses graph optimization algorithms to generate collision-free paths. The system automatically processes robot kinematics, builds configuration space graphs, and computes optimal paths without human intervention, thereby eliminating the time-consuming nature of manual planning while maintaining reliability in avoiding Cartesian limits and obstacles.
Solution Approach 2:
The robot system performs path planning autonomously using embedded processing devices that automatically generate collision-free paths based on the current environment and robot configuration. The system serves itself by continuously computing and updating paths without requiring external manual intervention, thus reducing planning time while ensuring reliable obstacle avoidance.
2Adaptability or versatility
If manual path planning is used, then paths can be customized for specific environments, but the process must be repeated frequently when environment changes
Solution Approach 1:
The patent implements dynamic path planning where the system continuously updates the configuration space graph and recomputes paths in response to environmental changes. The automated algorithm dynamically adapts to new obstacles, added chambers, or modified environments by regenerating paths based on the current state, enabling rapid reconfiguration without manual intervention and maintaining high productivity while preserving adaptability.
Solution Approach 2:
The system pre-computes and stores multiple possible paths in the configuration space graph during periods when environmental changes are not occurring. When changes occur, the system can quickly select or adjust from pre-computed paths rather than planning from scratch, thereby accelerating reconfiguration speed while maintaining the ability to adapt to specific environmental requirements.
3Productivity
If automated graph optimization is used, then path planning becomes fast and adaptable, but the computational complexity increases
Solution Approach 1:
The patent segments the complex path planning problem into distinct computational stages: building the configuration space graph, executing graph optimization algorithms to find multiple candidate paths, and selecting the optimal path based on criteria such as move time minimization. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining high planning speed and adaptability.
Solution Approach 2:
The system computes multiple candidate paths through graph optimization but does not exhaustively explore all possible paths. Instead, it generates a sufficient number of high-quality candidate paths and selects the best one based on predefined criteria, avoiding the excessive computational complexity of exhaustive search while still achieving fast and adaptable path planning.
Data Source
AI summary
A system includes a robot with a robot arm having multiple joints and an end effector to carry a substrate. A processing device is to build, with respect to a joint space for the multiple joints and the end effector, a graph of reachable positions and sub-paths between the reachable positions, wherein the reachable positions and the sub-paths satisfy Cartesian limits within the joint space. The processing device is to determine, by executing a graph optimization algorithm on the graph, multiple paths, each made up of a group of the sub-paths and having one of a shortest distance or a lowest cost between a start point and an end point of the end effector. The processing device is to select a path, of the multiple paths, through the graph that minimizes a move time of the end effector between the start point and the end point.