Rule Execution Engine for Real-Time Robotic Workcell Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual programming of robotic movements is tedious, time-consuming, and error-prone, and schedules generated for one workcell are often incompatible with different workcells due to varying physical properties.
Innovation Solution
A knowledge-based execution system with separated execution components, including an execution engine subsystem and an execution memory subsystem, that processes fact updates efficiently and drives robotic actions based on relevant rule conditions, allowing for real-time reactions and flexible operation across different workcell environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual programming is used to dictate robotic movements, then precise control of robotic components is achieved, but the programming process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The system enables robots to autonomously generate and adapt their own movement schedules based on sensor observations and rule-based reasoning, eliminating the need for manual programming. The execution system processes online observations and automatically determines robotic actions, allowing the system to serve itself without continuous human intervention.
Solution Approach 2:
The patent replaces manual mechanical programming with an automated execution system that uses sensor data, rule-based logic, and online observations to generate robotic schedules. This substitution of manual processes with automated computational systems eliminates the time-consuming nature of manual programming while maintaining precise control.
2Reliability
If manual programming is used for one workcell, then specific task requirements are met, but the schedule becomes incompatible with other workcells having different physical properties
Solution Approach 1:
The execution system dynamically adapts robotic schedules based on real-time sensor observations and workcell-specific conditions. Rather than using static manual programs, the system continuously processes online observations and adjusts robotic movements to match the actual physical properties and constraints of each workcell, enabling both reliability and adaptability.
Solution Approach 2:
The system changes operational parameters based on workcell characteristics by processing sensor data and applying rule-based logic. The execution system modifies movement parameters, speeds, and sequences according to the specific physical properties of each workcell, allowing the same system to reliably operate across different environments without manual reprogramming.
3Loss of information
If traditional execution systems process all fact updates, then complete information is maintained, but processing efficiency decreases when handling vast amounts of online observations
Solution Approach 1:
The execution system applies local quality by selectively processing only those fact updates that are relevant to current robotic tasks and rules. Rather than uniformly processing all observations, the system identifies and processes only the local subset of information that affects specific robotic actions, maintaining necessary information completeness while dramatically improving processing efficiency.
Solution Approach 2:
The system segments the processing of fact updates by organizing rules and observations into distinct categories and priority levels. This segmentation allows the execution system to process information in manageable chunks, applying different processing strategies to different types of observations, thereby maintaining information completeness while enhancing overall processing efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for rule execution in an online robotics system. One of the systems includes an execution engine subsystem and an execution memory subsystem. The execution engine receives rules having types and subtypes that represent a particular entity in an operating environment of a robot, provides subscription requests to the execution memory subsystem, and receives events emitted by the execution memory subsystem. The an execution memory receives subscription requests from the execution engine subsystem, receives new observations, converts the new observations into fact updates, performs pattern matching with the fact updates against the patterns of the subscription requests, and emits events to the execution engine subsystem for patterns that have been matched by the fact updates.