Finite-State Robot Control for Execution Quality Tracking
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Solution Overview
Problem
Predicting and ensuring the execution quality of industrial robot programs is difficult due to varying outcomes and multiple solution paths, leading to inefficient program creation, delayed error detection, and suboptimal performance, especially when errors such as broken robotic components or environmental changes occur.
Innovation Solution
Representing the industrial system as a finite state machine with nodes and edges, where each edge represents an action and has associated execution values that are modified based on outcomes, allowing for improved tracking and optimization of performance, enabling faster and more efficient error detection and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robot programs are created manually without performance tracking, then program creation is simpler, but execution quality and productivity are suboptimal
Solution Approach 1:
The system implements feedback by monitoring execution outcomes of robot program actions and using this information to dynamically update the state machine model. Execution values associated with state transitions are modified based on actual performance data, enabling continuous improvement of program quality while maintaining automated optimization without excessive complexity
Solution Approach 2:
The control system performs self-service by automatically tracking its own execution quality and optimizing program performance without external intervention. The system monitors its own actions, detects performance degradation, and adjusts execution values autonomously, improving productivity while keeping the control mechanism relatively simple
2Reliability
If error detection is performed ad-hoc, then detection flexibility is maintained, but error detection is delayed and productivity suffers
Solution Approach 1:
The system performs preliminary action by continuously monitoring execution quality and detecting errors before they significantly impact productivity. The state machine framework enables early detection of performance degradation by tracking execution values across state transitions, allowing preventive measures to be taken before errors accumulate and slow down task execution
3Adaptability or versatility
If multiple solution paths exist for robot tasks, then task flexibility increases, but predicting execution quality becomes more difficult
Solution Approach 1:
The system uses parameter changes by associating execution values with state transitions in the state machine. Different solution paths are represented as different sequences of state transitions, each with measurable execution values. This enables systematic comparison and prediction of execution quality across multiple paths by tracking parameters like execution time and success rates for each transition sequence
Data Source
AI summary
A method of controlling an industrial system including at least one agent, the method including providing a representation of the industrial system as a finite state machine, the state machine including a plurality of nodes and a plurality of edges, where each node represents a discrete system state of the industrial system, each edge represents an action for a state transition between system states represented by two of the nodes, and at least one execution value is associated with at least one of the edges; executing at least one action by the at least one agent, the at least one action being represented by at least one of the edges; and modifying at least one execution value associated with at least one of the edges representing the at least one executed action, based on an outcome of the at least one executed action.