Fog Drone Fleet Orchestrator with Dynamic Resource Allocation
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Solution Overview
Problem
Managing and coordinating a fleet of independent drones to perform complex tasks efficiently is challenging due to the difficulty in organizing, monitoring, and controlling multiple drones simultaneously without a central command.
Innovation Solution
An autonomous fog drone acts as a master drone, receiving job requests, determining the required resources and number of drones needed, recruiting drones, managing active and standby drones, and releasing them as necessary, all while providing cost estimates and updates to the dispatcher.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple independent drones are used to perform complex tasks, then productivity increases, but device complexity and difficulty of coordination increase
Solution Approach 1:
The patent introduces a central controller that acts as an intermediary between the dispatcher and the drone fleet. This controller receives job requests, determines resource requirements, recruits appropriate drones, and coordinates their activities. By inserting this intermediary layer, the system manages fleet complexity centrally while enabling multiple drones to work together efficiently on complex tasks.
Solution Approach 2:
The patent segments the drone fleet into specialized drones with specific functions (surveying, construction, material transport, etc.). Each drone is designed for particular tasks, and the controller assigns them based on job requirements. This segmentation allows the system to handle complex jobs by dividing them into specialized sub-tasks performed by appropriate drones, improving overall productivity while managing complexity through specialization.
2Productivity
If a fleet of specialized drones is recruited to perform complex jobs, then productivity increases, but operational costs increase
Solution Approach 1:
The patent implements dynamic fleet management where the controller adjusts the number and type of drones in the fleet based on real-time job requirements. Drones are recruited when needed and released when no longer required. This dynamic approach allows the system to scale drone usage according to task complexity and duration, maintaining high productivity for complex jobs while minimizing the number of drones operational at any given time to control costs.
Solution Approach 2:
The controller changes operational parameters by selecting different drone configurations based on job characteristics. For simple tasks, fewer drones are deployed; for complex tasks, more specialized drones are recruited. The system dynamically adjusts fleet composition, drone assignment, and operational parameters to optimize the balance between task completion capability and operational cost.
3Reliability
If drones are leased for extended periods to complete complex jobs, then task completion reliability improves, but operational costs increase
Solution Approach 1:
The controller performs preliminary assessment of job requirements and recruits the necessary drones before work begins. By determining the required fleet composition in advance and securing drone availability upfront, the system ensures reliable task completion while optimizing lease duration. This preliminary planning prevents both under-resourcing (which would compromise reliability) and over-resourcing (which would increase costs).
Data Source
AI summary
Embodiments herein describe a fog drone that selects, organizes, monitors, and controls a plurality of drones in a fleet. The fog drone receives a job to be completed from a dispatcher and identifies the resources for accomplishing the job such as the amount of material (e.g., fiber optic cable) or the type of drones (e.g., drones with RF antennas or digging implements) needed to execute the job. Using the identified resources, the fog drone estimates the number of drones needed to complete the job and can recruit available drones to form the fleet. Once the fleet is formed, the fog drone determines a number of drones to place on standby to replace active drones if those drones need to recharge or malfunction.