Joint-Space Graph Planning for Robot Arms in Confined Wafer Handling
Find Innovative SolutionsGenerate Solutions
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 uses a processing device to plan paths in robot joint space by building a graph of reachable positions and sub-paths within joint space, optimizing for shortest distances and minimizing move time while adhering to Cartesian and torque limits, using a graph optimization algorithm to select the most efficient path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual path planning is used for robot linkages in confined spaces, then the robot can avoid Cartesian limits and obstacles, but the path 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 determine robot paths. The system automatically processes joint space graphs, calculates optimal paths avoiding Cartesian limits, and generates motion commands without human intervention, thereby eliminating the time-consuming manual planning process while maintaining reliable obstacle avoidance.
Solution Approach 2:
The robot system performs self-path-planning by automatically generating and optimizing its own motion paths using embedded processing devices. The system independently constructs joint space graphs, identifies reachable positions, computes optimal paths avoiding obstacles, and executes motion commands without requiring external manual planning, enabling the robot to adapt and reconfigure paths autonomously when environmental changes occur.
2Adaptability or versatility
If manual path planning is used, then paths can be customized for specific environments, but the process must be repeated each time the environment changes
Solution Approach 1:
The patent implements dynamic path planning where the system continuously monitors environmental changes and automatically updates the joint space graph and path calculations in real-time. When obstacles are added, removed, or moved, or when processing chambers are reconfigured, the system dynamically regenerates optimal paths without requiring complete replanning from scratch, enabling rapid adaptation to environmental changes while maintaining customized path optimization.
Solution Approach 2:
The system performs preliminary construction of the joint space graph and pre-calculates multiple potential paths through the robot's workspace. When environmental changes occur, the system can quickly select and adjust from pre-computed path options or make minimal modifications to existing paths rather than performing complete path planning, significantly reducing reconfiguration time while maintaining adaptability to new environmental conditions.
3Ease of operation
If the robot operates in very small operational space with multiple joints, then the robot can perform complex movements, but the risk of running into Cartesian limits increases
Solution Approach 1:
The patent transforms the path planning problem from three-dimensional Cartesian space to multi-dimensional joint space, where each dimension represents a robot joint angle or position. This dimensional transformation allows the system to systematically account for all joint constraints and Cartesian limits simultaneously through graph-based representation, enabling reliable avoidance of obstacles while maintaining full complex movement capability across multiple joints and degrees of freedom.
Data Source
AI summary
A system includes a robot arm with multiple joints and one or more end effector to carry a substrate. A processing device determines, within joint space of the robot arm, start/end points of the one or more end effector for a complete movement. The processing device builds, in joint space for the multiple joints and the one or more end effector, a graph of reachable positions and sub-paths between the reachable positions that satisfy Cartesian limits. The reachable positions are identified at a granularity that divides the complete movement into multiple sub-movements. The processing device executes a graph optimization algorithm on the graph to determine multiple paths, each a group of the sub-paths, that have one of shortest distances or lowest costs between the start/end points, and selects a path thereof that minimizes move time of the one or more end effector between the start/end points.