Autonomous Fleet Control With Persistent Mission Command Links
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Solution Overview
Problem
Existing fleet management systems for autonomous vehicles lack efficient demand anticipation and control mechanisms, particularly for autonomous flying vehicles, leading to suboptimal resource allocation and operational inefficiencies.
Innovation Solution
A computer-implemented method and system for controlling a fleet of autonomous vehicles, which involves obtaining missions from a master schedule, generating vehicle commands based on mission parameters, maintaining persistent connections with vehicles, sending commands for mission execution, and monitoring vehicle operations to adjust commands as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized fleet management systems are implemented to control large autonomous vehicle fleets, then resource allocation and operational control improve, but system complexity and cost increase
Solution Approach 1:
The fleet management system is segmented into multiple hierarchical levels: centralized cloud-based components for strategic decision-making and local onboard controllers for tactical execution. This segmentation allows the system to handle large fleets without overwhelming central infrastructure, improving scalability while maintaining control efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-planning flight paths, pre-coordinating with air traffic control, and pre-positioning vehicles based on predictive analytics. This reduces real-time computational burden and enables faster response to dynamic conditions without increasing system complexity.
2Reliability
If real-time monitoring and control of autonomous vehicles is implemented, then operational safety and mission execution improve, but communication requirements and system resources increase
Solution Approach 1:
The system implements periodic status reporting and command transmission instead of continuous communication. Vehicles report key parameters at scheduled intervals or upon significant events, reducing communication overhead and energy consumption while maintaining adequate monitoring for safe operation.
Solution Approach 2:
Vehicles autonomously monitor their own status, detect anomalies, and execute corrective actions without constant controller intervention. This self-service capability reduces communication requirements while maintaining high reliability through onboard decision-making algorithms.
3Productivity
If predictive analytics and demand anticipation systems are added to fleet control, then resource allocation optimization improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs predictive analytics and demand forecasting in advance using historical data and machine learning models. By pre-computing resource allocation strategies and vehicle positioning recommendations, the system reduces real-time computational requirements while maintaining optimization benefits.
Solution Approach 2:
Complex real-time computational problems are replaced with pre-computed lookup tables and simplified decision rules derived from predictive analytics. This substitution reduces processing time during critical operations while retaining the benefits of sophisticated optimization.
Data Source
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AI summary
An method for controlling an autonomous vehicle fleet, including obtaining (502), by a fleet controller (104), from a master schedule (106), a mission for a vehicle (112) of a fleet of autonomous vehicles, where the mission is associated with a mission entry of the master schedule, generating (506) vehicle commands according to mission parameters associated with the mission, maintaining a persistent connection with the vehicle, sending (508) the vehicle commands to the vehicle using the connection, the vehicle commands causing the vehicle to execute the mission under control of the fleet controller, and monitoring (512) operation of the vehicle during performance of the mission.