Flying Vehicle Sensor Fusion for 3D Obstacle Navigation
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Solution Overview
Problem
Current air vehicles face challenges in navigation and obstacle detection, particularly in complex 3D environments, where existing systems struggle to generate accurate 3D models and respond effectively to dynamic obstacles, leading to inefficiencies and safety concerns.
Innovation Solution
The implementation of a system that uses LIDAR, radar, and camera images to generate multi-dimensional models of the environment, allowing for hand control gestures to control vehicle operations, and employs edge processing with machine learning to create high-resolution 3D maps and detect obstacles, enabling real-time navigation and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR, radar, and camera images are used to generate multi-dimensional models, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The patent combines LIDAR, radar, and camera sensors into an integrated sensing system that generates multi-dimensional models by fusing data from multiple sources. This merging approach achieves high measurement precision through complementary sensor data while managing complexity through unified processing architecture.
Solution Approach 2:
The sensor system is designed to perform multiple functions: LIDAR provides depth mapping, radar detects obstacles and weather conditions, and cameras capture visual information. This multi-functional arrangement achieves comprehensive environmental perception without requiring separate dedicated systems for each function.
2Productivity
If edge processing with machine learning is implemented, then productivity and response time are improved, but use of energy increases
Solution Approach 1:
The processing architecture is segmented into edge processing units distributed throughout the vehicle and centralized cloud processing. Machine learning algorithms run locally at the edge for time-critical navigation decisions, while less time-sensitive data is processed centrally. This segmentation achieves real-time responsiveness for critical functions while managing overall energy consumption.
Solution Approach 2:
The system dynamically adjusts processing intensity and energy consumption based on operational context. Machine learning models are activated selectively based on environmental complexity, obstacle density, and navigation requirements, optimizing the balance between productivity and energy use.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enhances the efficiency and safety of air vehicle navigation by providing accurate 3D models and real-time obstacle detection, allowing for precise control and collision avoidance in dynamic environments.
Implementation Method 1
Based on LIDAR, radar, and camera images, the system can generate 3D models for navigation purposes
Implementation Method 2
Based on LIDAR, radar, and camera images, the system can generate 3D models for navigation purposes
Data Source
AI summary
A transport method includes providing a cab having a moveable actuator coupled to the propulsion unit to move the propulsion unit between a first position above the cab during take-off and a second position during lateral flight; receiving a hand control from a controller or stick, and flight data captured by a plurality of cameras or sensors, wherein hand grip or movement represents a control request; and determining control options based on a current state of the control request and the environment of the cab.