Drone Flight Plan Adaptation for Dynamic Approach Coordination
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Solution Overview
Problem
Current drone flight management systems lack efficient methods for dynamically adapting flight plans and coordinating drone approaches to ensure safe and efficient operations, particularly in complex environments with varying conditions and obstacles.
Innovation Solution
The implementation of a drone control platform that allows for real-time modification of flight plans based on received data from drones, including sensor data and environmental conditions, and the use of autonomous drone landing devices (ADLDs) to guide drones into predefined approach corridors, ensuring safe landing and takeoff procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a drone executes a pre-defined flight plan autonomously, then operational efficiency is improved, but the ability to adapt to dynamic environmental conditions and obstacles deteriorates
Solution Approach 1:
The flight plan is transformed from a static pre-defined sequence to a dynamic structure that can be modified in real-time. The system continuously receives sensor data from the drone and external environmental information, then dynamically adjusts flight parameters, waypoints, and mission objectives during execution to adapt to changing conditions while maintaining autonomous operation.
Solution Approach 2:
A feedback loop is established where sensor data from the drone (position, speed, altitude) and external environmental data (weather, obstacles, airspace restrictions) are continuously monitored. This feedback information is processed by the flight management system to determine necessary modifications to the flight plan, which are then transmitted back to the drone for implementation.
2Adaptability or versatility
If real-time modification of flight plans is implemented, then adaptability to environmental conditions is improved, but system complexity and data processing requirements deteriorate
Solution Approach 1:
The flight management system is segmented into modular components: a flight plan generator that creates initial plans, a data receiver that collects sensor and environmental information, a decision module that determines necessary modifications, and a communication interface that transmits updates to the drone. This segmentation reduces overall system complexity by allowing each component to specialize in specific functions.
Solution Approach 2:
A flight management server or ground control station acts as an intermediary between the drone and the flight plan modification process. This intermediary receives raw sensor data, processes it against predefined criteria and constraints, generates modified flight plans, and transmits them to the drone, thereby simplifying the architecture by centralizing complex processing logic outside the drone itself.
3Adaptability or versatility
If multiple drones are coordinated within a location, then operational versatility is improved, but coordination complexity and communication overhead deteriorate
Solution Approach 1:
The flight management system is designed with universal protocols and data structures that can handle any number of drones with any flight controller type. The system uses standardized communication interfaces and agnostic flight plan formats that work across different drone platforms, allowing multi-drone coordination without requiring drone-specific customization for each unit.
Solution Approach 2:
Instead of implementing full bidirectional real-time communication between all drones, the system uses unidirectional flight plan distribution from the ground station to each drone. Each drone executes its assigned flight plan independently with minimal inter-drone communication, reducing coordination complexity while still achieving synchronized multi-drone operations through centralized management.
4Adaptability or versatility
If flight plans are made agnostic to different flight controller types, then system versatility is improved, but the precision of controller-specific optimization deteriorates
Solution Approach 1:
The system uses parameter-based adaptation where the agnostic flight plan contains standardized commands and parameters that are automatically adjusted based on the specific flight controller type detected. The ground station identifies the drone's controller model and modifies relevant parameters (communication protocols, control rates, waypoint formats) to optimize performance for that specific controller while maintaining the overall agnostic structure of the flight plan.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for sending a flight plan for execution by a drone, where the flight plan is adapted to a flight controller of the drone. Receiving flight data from the drone while the drone is executing the flight plan. Determining a modification to the flight plan based on the flight data received from the drone. Sending the modification to the flight plan to the drone while the drone is executing the flight plan, such that the drone executes the flight plan as modified by the modification.