Local Drone Navigation Using On-Site Guidance for Precise Drop-Off
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Solution Overview
Problem
Current drone delivery systems are ill-equipped to navigate and deliver packages in dense urban areas with multiple buildings and limited spaces, such as apartments or condominiums, as they rely on centralized drop-off locations and lack precision for specific target locations within properties.
Innovation Solution
A system and method that integrates local on-site computing devices to provide UAVs with precise navigation and coordination data, allowing them to autonomously navigate to specific micro-destinations on properties, such as balconies or porches, by using GPS-based navigation initially and then switching to local device-based navigation for final approach, avoiding obstacles and ensuring accurate delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If centralized drop-off locations are used for drone delivery, then delivery coverage area is expanded, but delivery precision to specific target locations deteriorates
Solution Approach 1:
The navigation system is segmented into two levels: centralized GPS-based navigation for macro-positioning to the general property area, and local on-site signal-based navigation for micro-positioning to the specific delivery location (balcony, porch, etc.). This segmentation allows the system to achieve both broad coverage and high precision by dividing the navigation task into manageable segments.
Solution Approach 2:
Local on-site computing devices act as intermediaries between the centralized drone control system and the final delivery location. These devices receive navigation signals, process them locally, and provide real-time guidance to the drone for the final approach, enabling precise delivery to specific micro-locations while maintaining centralized coordination.
2Device complexity
If GPS-based navigation is used alone, then navigation simplicity is maintained, but navigation precision in dense urban areas deteriorates
Solution Approach 1:
The navigation system divides the delivery process into two phases: Phase 1 uses simple GPS-based navigation for macro-positioning to the general property area, and Phase 2 transitions to local on-site signal-based navigation for micro-positioning. This segmentation maintains simplicity where possible while adding precision where needed.
Solution Approach 2:
The system performs preliminary GPS-based navigation to bring the drone into proximity of the target property before engaging the more complex local on-site navigation system. This preliminary action reduces the distance and complexity of the final precision approach.
3Measurement precision
If visual odometry is used for navigation, then navigation precision is improved, but computational complexity increases
Solution Approach 1:
The system replaces visual odometry (which requires complex computer vision processing and computational resources) with radio signal-based navigation. The local computing devices emit navigation signals that the drone receives and processes, providing precise navigation without the computational burden of visual odometry algorithms.
Solution Approach 2:
The navigation system changes the fundamental parameter used for positioning: instead of relying on visual feature tracking and odometry calculations, it uses signal emission and reception parameters. This parameter change from visual to electromagnetic signal-based navigation reduces computational complexity while maintaining precision.
4Device complexity
If drone navigation is designed for rural areas, then navigation simplicity is maintained, but adaptability to urban environments deteriorates
Solution Approach 1:
The navigation system is designed with multi-functionality to operate in both rural and urban environments. It uses GPS-based navigation as a universal baseline that works in open areas, and incorporates local on-site computing devices that activate in urban environments to provide additional precision navigation, making the system adaptable to different environmental contexts.
Solution Approach 2:
The system dynamically adjusts its navigation approach based on environmental context. In open rural areas, it relies solely on GPS navigation. In dense urban areas with multiple buildings and obstructions, it dynamically engages the local on-site signal-based navigation system to provide real-time guidance, making the navigation approach flexible and adaptive to environmental conditions.
Data Source
AI summary
A system and method for high-precision automated guidance of a drone via a local (on-site) computing device for delivery of items is disclosed. The drone navigates to a location in proximity to the recipient's home using standard navigation protocols. Once the UAV arrives at this local destination, communication between the UAV and an on-site computing device occurs. The on-site device serves as a micro air-traffic controller to the UAV's precise drop off target location. The on-site device can provide approach vectors, guidance around obstacles and navigation instructions to a final micro-destination.