Air Vehicle Gesture Control With 3D Obstacle Navigation
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Solution Overview
Problem
Current air vehicles face challenges in autonomous navigation and obstacle detection, particularly in complex environments, due to limitations in sensor data processing and real-time decision-making, which can lead to accidents and inefficiencies.
Innovation Solution
The implementation of a system that generates multi-dimensional models of air vehicles operating in 3D environments using LIDAR, radar, and camera images, allowing for hand control gestures to control vehicle operations, and utilizing edge processors and neural networks for real-time obstacle detection and navigation, with crowd-sourced 3D mapping and vehicle-to-vehicle communication for enhanced safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If advanced sensor data processing and neural networks are implemented for autonomous navigation, then obstacle detection capability is improved, but device complexity increases
Solution Approach 1:
The patent introduces edge processors as intermediary devices that perform neural network inference offloaded from the main vehicle computer. These edge processors act as mediators between the sensor data acquisition system and the central control system, enabling complex obstacle detection while distributing computational complexity to specialized hardware components.
Solution Approach 2:
The system segments the autonomous navigation function into multiple independent modules: sensor data acquisition, edge processing for real-time obstacle detection, neural network-based decision making, and vehicle control. This segmentation allows each module to be optimized independently and reduces overall system complexity by distributing functions across separate computational units.
2Productivity
If real-time decision-making is implemented for autonomous navigation, then operational efficiency is improved, but processing time requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data at the edge devices before transmission to the central system, and by pre-computing neural network models offline. This preliminary processing reduces the real-time computational burden during critical decision-making moments, enabling faster response times while maintaining high operational efficiency.
Solution Approach 2:
The patent replaces traditional mechanical decision-making systems with electronic neural network-based systems that process information at electronic speeds. This substitution enables real-time decision-making by leveraging the vastly faster processing capabilities of electronic systems compared to mechanical or human-operated systems.
3Ease of operation
If hand control gestures are used for vehicle control, then ease of operation is improved, but control precision may be reduced
Solution Approach 1:
The system implements feedback mechanisms where the vehicle responds to hand gestures with confirmation signals and incremental control adjustments. The feedback loop allows the operator to refine control commands through sequential gestures, with the system providing real-time status information to ensure precise control despite the indirect nature of gesture-based input.
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 improved autonomous navigation, enhanced obstacle detection, and optimized route planning, reducing the risk of accidents and increasing operational efficiency by leveraging advanced sensor data processing and communication technologies.
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
Systems and methods are disclosed for controlling a vehicle by generating a multi-dimensional model of a vehicle operating in a 3D environment; determining a hand control gesture as captured by a plurality of cameras or sensors in the vehicle, wherein a sequence of finger, palm or hand movements represents a vehicle control request; determining vehicle control options based on the model, a current state of the vehicle and the environment of the vehicle; and controlling the vehicle to operate based on the model and the 3D environment.