Software CoBot Framework Using Segmented Brain Model
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Solution Overview
Problem
Current software development and bot development frameworks face challenges in implementing frameworks for software CoBot solutions, particularly in supporting attributes like shared awareness, distributed governance, interoperability, trust, and other collaborative features for human-robot collaboration.
Innovation Solution
A framework is implemented that supports the engineering and execution of software CoBots using a distributed primary and secondary CoBot brain model for real-time monitoring, enabling automated goal detection, team design, responsibility allocation, and deployment, while tracking performance and adapting processes in case of complications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a framework for software CoBot solutions is implemented to support collaborative features, then human-bot collaboration effectiveness is improved, but device complexity increases
Solution Approach 1:
The CoBot framework is segmented into distinct functional modules including bot development environment, runtime monitoring system, and collaborative feature engines. Each module operates independently but communicates through standardized interfaces, allowing the complex system to be managed through modular components rather than monolithic architecture.
Solution Approach 2:
A runtime monitoring system acts as an intermediary layer between the bot execution environment and the collaborative feature management system. This mediator handles coordination, communication, and conflict resolution between multiple bots and human operators, reducing the complexity burden on individual bot developers while enabling rich collaborative capabilities.
2Productivity
If real-time monitoring and adaptive processes are implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The runtime monitoring system implements periodic sampling and event-triggered monitoring rather than continuous real-time analysis. Monitoring intensity is adjusted based on bot activity levels and criticality, with full real-time monitoring reserved for critical events while routine operations use reduced-frequency sampling, thereby maintaining productivity while reducing computational energy consumption.
Solution Approach 2:
The monitoring system dynamically adjusts its resource consumption based on operational context. During high-priority tasks, monitoring intensity increases to ensure productivity, while during routine operations, resource allocation is reduced. This dynamic adaptation allows the system to optimize the trade-off between productivity gains and energy consumption based on current operational requirements.
Data Source
AI summary
In some examples, software CoBot engineering, execution, and monitoring may include extracting a CoBot requirement from a requirement specification for a CoBot that is to be implemented. Based on application of a CoBot description language to the CoBot requirement, a CoBot workflow that specifies a plurality of tasks to be performed by the CoBot may be generated. A determination may be made as to whether a task is to be performed by a bot or by a human. A team that includes a plurality of bots and at least one human may be generated to execute the CoBot workflow. The bots of the team may be prioritized to identify a bot that is a best match to the CoBot requirement. The CoBot that includes configured bots may be deployed in an operational environment to perform the CoBot workflow.