Multi-Entity Autonomy State Control for Reduced Remote Crew Load
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Solution Overview
Problem
Current systems for controlling multiple unmanned vehicles require significant human intervention, leading to increased workload for remote crews, as they lack efficient mechanisms to manage autonomy levels and reduce operator involvement during autonomous operations.
Innovation Solution
A system comprising multiple mobile entities and a master entity, where each mobile entity operates in one of three states: autonomous execution of a mission plan, preliminary control command transmission for human review, and requiring explicit human input, allowing for reduced crew workload by autonomously managing tasks and alerting operators only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mobile entities operate with high autonomy to reduce crew workload, then productivity and ease of operation are improved, but reliability may deteriorate due to reduced human monitoring and intervention capability
Solution Approach 1:
The control module dynamically transitions between three operational states (autonomous, preliminary control, full control) based on mission progress and environmental conditions. This dynamic adaptability allows the system to maximize autonomy when safe while maintaining human oversight when needed, resolving the contradiction between productivity and reliability
Solution Approach 2:
The system continuously monitors mission status, environmental conditions, and control module performance, providing feedback that triggers state transitions. This feedback mechanism ensures that autonomy is maintained only when conditions permit, while automatically escalating human involvement when reliability concerns arise, balancing productivity gains with safety assurance
2Productivity
If the control module operates autonomously in the first state, then productivity increases and ease of operation improves, but device complexity increases due to the need for sophisticated autonomous control algorithms
Solution Approach 1:
The control system is segmented into three distinct operational states with clearly defined functions and decision boundaries. Each state handles specific aspects of mission control, breaking down the complex autonomous control problem into manageable segments that can be implemented and verified independently, reducing overall system complexity while maintaining productivity
Solution Approach 2:
The system implements partial autonomy rather than complete autonomy, using full autonomous control only when necessary for productivity while relying on human operators for other phases. This partial action approach achieves productivity benefits without requiring the full complexity of completely autonomous operation across all mission phases
3Adaptability or versatility
If the system transitions between multiple operational states, then adaptability improves and productivity increases, but device complexity increases due to state management requirements
Solution Approach 1:
The system employs dynamic state transitions based on real-time assessment of mission status and environmental conditions. Predefined transition criteria automatically trigger state changes, providing operational flexibility and adaptability without requiring complex manual state management, as the system self-regulates its operational mode based on objective conditions
Data Source
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AI summary
A system (10) for controlling operation of a plurality of mobile entities (100) is described. The system includes a plurality of mobile entities (100) and a master entity (50) that is communicatively coupled to each one of the plurality of mobile entities (100). Each of the mobile entities (100) includes a control module (110) and at least one function component (120). The control module (110) controls operation of the at least one function component (120) and operates in either a first state, a second state, or a third state. Depending on the state in which the control module is operated, the function components (120) are controlled either by the control module (110) in an autonomous manner, or control of the function components (120) is partly or entirely assigned to a human operator.