Dynamic Safety Requirements for Adaptive Robotics Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Safety Requirements Specifications (SRS) documents are static, limiting flexibility during system operation and requiring burdensome re-establishment of system safety with every change in functionality or environment.
Innovation Solution
A safety computing system that includes an ontology and reasoning engine to dynamically update safety-related ontologies throughout the lifecycle of robotics or automation systems, allowing for just-in-time verification of safety constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static safety requirements specifications are used, then safety constraints are clearly defined and enforced, but system flexibility and adaptability to changes are reduced
Solution Approach 1:
The patent transforms static safety requirements into dynamic, executable code that can be automatically updated and verified. The safety requirements specification is converted into a machine-executable form that allows real-time adaptation while maintaining enforcement, resolving the contradiction between static safety assurance and dynamic flexibility.
Solution Approach 2:
The system allows safety parameters to be modified through automated updates of the executable safety requirements. Changes in safety constraints can be made by updating the code representation, enabling flexible parameter adjustment while maintaining rigorous safety verification through automated checking.
2Reliability
If static safety requirements are enforced, then safety violations are prevented, but verification of requirements during operation is inhibited
Solution Approach 1:
The system implements automated feedback loops where the executable safety requirements are continuously verified against system operation. The machine-executable form enables automatic checking and reporting of compliance status, making verification easy while maintaining prevention of violations through real-time monitoring.
Solution Approach 2:
The safety verification process is automated through the executable code representation, which self-verifies compliance during operation without requiring manual intervention. The system automatically checks safety constraints and reports violations, enabling easy verification while maintaining rigorous safety enforcement.
3Adaptability or versatility
If safety requirements are changed, then adaptability to new environments is improved, but system safety must be re-established from scratch
Solution Approach 1:
The safety requirements are pre-converted into machine-executable code during system design, creating a ready-to-verify foundation. When environmental changes occur, the executable representation allows rapid updates and automated re-verification, eliminating the need to re-establish safety from scratch and significantly reducing the time loss.
4Productivity
If engineering time and costs are reduced through agile safety management, then productivity increases, but safety verification rigor may be compromised
Solution Approach 1:
The patent replaces manual, mechanical safety verification processes with automated machine-executable verification. The executable safety requirements code automatically checks compliance during operation, providing rigorous verification without requiring time-consuming manual processes, thus increasing productivity while maintaining or enhancing safety rigor.
Data Source
AI summary
Methods and systems are disclosed for generating or monitoring programmatically accessible safety requirements dynamically during a systems lifecycle. In an example aspect, a safety computing system includes one or more processors and a memory having a plurality of application modules stored thereon. The modules can include an ontology and reasoning engine configured to update safety-related ontologies related to components of a robotics or automation system throughout the systems lifecycle. The safety computing system can further include a design module, an implementation module, and an operations module each communicatively coupled to the ontology and reasoning engine.


