Flying Vehicle Sensor Fusion for 3D Obstacle Detection
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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 vehicle's environment, allowing for hand control gestures to control the vehicle and crowd-source 3D map data, with edge processing capabilities and neural networks for real-time obstacle detection and avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR, radar, and camera images are used to generate multi-dimensional models for real-time obstacle detection, then measurement precision and reliability are improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent combines LIDAR, radar, and camera systems into an integrated sensor suite that works together to generate multi-dimensional models of the environment. This merging of multiple sensing modalities allows the system to achieve high measurement precision for obstacle detection while sharing processing infrastructure and data fusion algorithms, thereby managing the complexity that would arise from operating these systems independently.
Solution Approach 2:
The sensor system is designed to perform multiple functions: LIDAR provides depth mapping, radar detects moving objects and weather conditions, and cameras capture visual information. This multi-functionality allows a single integrated system to handle various detection tasks simultaneously, improving obstacle detection precision without proportionally increasing device complexity, as the same hardware infrastructure supports diverse sensing capabilities.
2Productivity
If edge processing capabilities and neural networks are implemented for real-time data processing, then productivity and response time are improved, but use of energy and device complexity increase
Solution Approach 1:
The processing architecture is segmented into edge processing units distributed across the vehicle and centralized neural network processors. This segmentation allows real-time processing of critical data locally at the edge with low latency, while less time-sensitive computations are handled by centralized systems. This distributed approach improves overall productivity and response time while managing energy consumption by avoiding centralized processing of all data streams.
Solution Approach 2:
The system performs preliminary processing of sensor data at the edge before transmitting to centralized systems. By pre-processing and filtering data locally, the system reduces the computational burden on energy-intensive centralized neural networks, thereby improving real-time response capability while conserving overall system energy consumption.
3Ease of operation
If hand control gestures are used for vehicle control, then ease of operation is improved, but reliability may be affected by misinterpretation of gestures
Solution Approach 1:
The gesture recognition system incorporates feedback mechanisms where the system confirms detected gestures to the operator and provides feedback on control actions taken. This feedback loop allows the operator to verify that their gestures were correctly interpreted, and to correct misinterpretations before they lead to erroneous control actions, thereby maintaining ease of operation while improving reliability of control signal execution.
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 enables precise navigation and obstacle avoidance, enhancing safety and efficiency by providing real-time data processing and adaptive control, even 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 method for transportation includes providing a vehicle with a cab and having a moveable actuator coupled to a propulsion unit to move the propulsion unit between a first position above the cab during lift-off and a second position during lateral flight. The system can receive hand control gestures as captured by cameras/sensors. A flight computer determines vehicle control options based on the model, a current state of the vehicle and the environment of the vehicle.