Cloud UAV Tasking for Low-Latency Fleet Assignment
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Solution Overview
Problem
Managing fleets of drones has become increasingly complex due to the need for planning and scheduling missions, maintenance activities like battery charge management and software updates, and handling sensor and telemetry data, which often requires additional latency and custom software applications.
Innovation Solution
A tasking service for UAVs that receives task parameters, continuously evaluates the present state of UAVs against desired states, and assigns tasks when a match is found, allowing for direct wireless connectivity to the Internet and reducing the need for local controllers or custom software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a dedicated controller or ground station is used to bridge communication between drones and computing devices, then communication functionality is achieved, but system complexity increases and latency is introduced
Solution Approach 1:
The patent extracts the communication bridging function from the dedicated controller/ground station and relocates it to the cloud-based task management service. The drone directly communicates with the computing device through network interfaces, eliminating the need for intermediate hardware controllers. This extraction reduces device complexity while maintaining communication reliability through cloud-based task parameter transmission.
Solution Approach 2:
The patent introduces a cloud-based task management service as a software intermediary that replaces the hardware controller/ground station. This software mediator handles task parameter transmission, state information reception, and data forwarding between drones and computing devices, reducing system complexity while maintaining communication functionality through standardized protocols.
2Reliability
If a dedicated controller or ground station is used to bridge communication, then communication functionality is achieved, but latency increases in video and data feeds
Solution Approach 1:
The patent removes the latency-introducing hardware controller/ground station from the communication path and replaces it with direct network communication between drones and computing devices. Task parameters and state information are transmitted through cloud-based services that optimize data flow, reducing transmission latency while maintaining communication reliability.
Solution Approach 2:
The patent replaces the mechanical/hardware-based controller system with a software-based cloud communication architecture. This substitution eliminates the processing and transmission delays inherent in hardware controllers, achieving lower latency through optimized network protocols and direct digital communication paths between drone components and computing devices.
3Adaptability or versatility
If custom software applications are required on devices for drone access and control, then full functionality is achieved, but ease of operation deteriorates
Solution Approach 1:
The patent implements a universal cloud-based task management service that can be accessed through standard web browsers and common communication protocols. Instead of requiring custom software applications on each device, the service provides drone functionality access through standardized interfaces, making the system easier to operate while maintaining full adaptability to different computing devices and platforms.
Solution Approach 2:
The patent enables devices to access and control drones through self-service cloud-based interfaces without requiring pre-installed custom software. The task management service automatically handles authentication, task parameter transmission, and data forwarding, allowing users to operate drones through standard web browsers or common applications, thereby improving ease of operation while preserving full functionality access.
Data Source
AI summary
Technology is disclosed herein for operating a tasking service for UAVs. In an implementation, a tasking service receives task parameters which includes a desired state of the UAVs for performing a task and service information associated with performing the task. The tasking service continuously receives state information from the UAVs which identifies a present state of the UAVs and continuously evaluates the present state of the UAVs with respect to the desired state. When the present state of an UAV matches the desired state, the tasking service assigns the task to the UAV and provides the service information to the UAV. In an implementation, the tasking service receives task parameters via an application programming interface from a client application in communication with the tasking service.