Robot Work-Volume Planning for Collision-Free Scheduling
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Solution Overview
Problem
Manual programming of robotic movements in industrial settings is tedious, time-consuming, and error-prone, and often fails to generate efficient schedules that can be reused across different workcells due to their unique physical constraints.
Innovation Solution
A system that generates robotics schedules using an underconstrained process definition graph, iteratively applying transformers to relax and reintroduce constraints, ensuring collision avoidance and optimizing task assignment and movement paths for multiple robots within a workcell.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual programming is used to dictate robotic movements, then the schedule can be customized for specific tasks, but the programming process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The patent replaces manual mechanical programming with an automated system that uses sensors, processors, and algorithms to generate robotic schedules. The system automatically detects workcell properties, models robot movements, and optimizes schedules without human intervention, eliminating the tedious and error-prone manual programming process while maintaining high accuracy through computational methods
Solution Approach 2:
The system enables self-service by allowing the robotic scheduling system to automatically program and optimize its own operations. The processor autonomously generates schedules based on sensor data and workcell characteristics, eliminating the need for external manual programming and enabling the system to adapt and refine its own scheduling algorithms continuously
2Manufacturing precision
If manual programming is used for one workcell, then the schedule can be optimized for that specific environment, but it cannot be reused for other workcells with different physical properties
Solution Approach 1:
The patent creates a universal scheduling system that can operate across multiple workcells with different physical properties. The system uses generic algorithms and models that adapt to various workcell configurations through automated sensing and parameter adjustment, allowing the same core system to optimize schedules for diverse environments without requiring separate manual programming for each workcell
Solution Approach 2:
The system dynamically adjusts scheduling parameters based on detected workcell properties. Sensors automatically measure physical dimensions, robot positions, and environmental characteristics, and the processor modifies schedule parameters accordingly. This allows the system to maintain optimized performance across different workcells by automatically adapting parameters rather than requiring fixed manual programming
3Productivity
If multiple robots operate simultaneously in a workcell, then task completion time is reduced, but the risk of collisions between robots increases
Solution Approach 1:
The patent introduces a central processor as an intermediary that coordinates movements between multiple robots. The processor acts as a mediator that receives data from all robots and sensors, computes collision-free paths and timing, and issues coordinated commands to each robot. This intermediary system enables multiple robots to operate simultaneously at high speed while maintaining safety through centralized oversight and real-time path planning
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor robot positions, velocities, and environmental conditions in real-time. This feedback is fed back to the processor, which dynamically adjusts schedules and robot paths to prevent collisions. The closed-loop control system maintains high productivity by allowing rapid robot operations while ensuring safety through real-time monitoring and adaptive path planning
4Reliability
If the search space in 6D coordinate system is searched exhaustively to avoid collisions, then collision-free schedules can be found, but the computation time becomes unreasonably long
Solution Approach 1:
The patent segments the complex 6D coordinate search space into smaller, more manageable subspaces or dimensions. The system divides the search problem into discrete steps, breaking down the exhaustive search into sequential or parallel sub-problems that can be solved more efficiently. This segmentation reduces computational complexity while maintaining collision avoidance through systematic exploration of divided search spaces
Solution Approach 2:
The system performs partial searches or uses heuristic methods that explore only the most promising portions of the search space rather than exhaustively searching all possibilities. By applying intelligent pruning, constraint propagation, and heuristic guidance, the system finds sufficiently good collision-free schedules in reasonable time without requiring complete exhaustion of the entire 6D coordinate space, accepting near-optimal solutions in exchange for computational feasibility
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning by work volumes to avoid conflicts. One of the methods includes receiving a process definition graph for a robot that includes action nodes, wherein the action nodes include (1) transition nodes that represent a motion to be taken by the robot from a respective start location to an end location and (2) task nodes that represent a particular task to be performed by the robot at a particular task location. An initial modified process definition graph that ignores one or more conflicts between respective transition nodes as well as one or more conflicts between respective transition nodes and task nodes is generated from the process definition graph. A refined process definition graph that ignores conflicts between transition nodes and recognizes conflicts between transition nodes and task nodes is generated from the initial modified process definition graph.