UAV Obstacle Avoidance Using Adaptive Binocular-Monocular Detection
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Solution Overview
Problem
Current obstacle detection and avoidance systems for Unmanned Aerial Vehicles (UAVs) are limited in their ability to effectively detect obstacles at both short and long distances, necessitating a solution that can switch between different detection modes to optimize obstacle detection across various ranges.
Innovation Solution
A method and apparatus that determine the detection mode based on the disparity between two images captured by an imaging device, switching between binocular and monocular modes depending on the disparity threshold, allowing for accurate obstacle detection and avoidance at both short and long distances by using binocular triangulation for closer objects and monocular triangulation for more distant ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binocular mode is used for obstacle detection, then measurement precision is improved for close objects, but device complexity increases and energy consumption increases
Solution Approach 1:
The system dynamically switches between binocular and monocular detection modes based on real-time disparity analysis. When objects are within the effective binocular range (disparity >= threshold), the system uses binocular mode for high precision. When objects are beyond this range (disparity < threshold), it switches to monocular mode, thereby adapting the system complexity to the actual detection needs.
Solution Approach 2:
The system changes the detection parameter (detection mode) based on the disparity value. By comparing the calculated disparity with a predetermined threshold, the system selects the appropriate detection mode (binocular or monocular), optimizing the balance between measurement precision and device complexity for different distance scenarios.
2Measurement precision
If binocular mode is used for obstacle detection, then measurement precision is improved for close objects, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts energy consumption by switching detection modes based on disparity. Binocular mode, which consumes more energy, is only activated when objects are within the effective detection range. For distant objects, the system uses the lower-energy monocular mode, thereby optimizing energy usage while maintaining detection precision when needed.
Solution Approach 2:
The system changes the energy consumption parameter by selecting different detection modes based on disparity threshold comparison. This parameter change strategy ensures that high-energy binocular processing is only performed when it provides actual value (close objects), while low-energy monocular processing handles distant objects.
3Device complexity
If monocular mode is used for obstacle detection, then device complexity is reduced, but measurement precision deteriorates for close objects
Solution Approach 1:
The system dynamically selects the appropriate detection mode based on real-time disparity analysis. For close objects where precision is critical (disparity >= threshold), binocular mode is activated. For distant objects where monocular mode suffices (disparity < threshold), the system uses the simpler detection approach, thereby maintaining precision when needed while reducing complexity when acceptable.
4Device complexity
If a single detection mode is used for all distances, then device complexity is reduced, but adaptability deteriorates across different distance ranges
Solution Approach 1:
The system achieves multi-functionality by implementing both binocular and monocular detection modes within a single detection system. The mode selection mechanism allows the system to universally handle both close-range (binocular) and long-range (monocular) detection scenarios, making the system adaptable to various distance ranges while maintaining reasonable complexity through intelligent mode switching.
Solution Approach 2:
The system dynamically adapts its detection capability based on the distance to objects by analyzing disparity values. This dynamic adaptation allows the system to optimize its performance for different distance ranges, achieving versatility without requiring separate dedicated systems for each range.
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
Enables optimized obstacle detection and avoidance across a wide range of distances by selecting the appropriate detection mode based on image disparity, ensuring precise navigation and avoidance of obstacles regardless of their proximity to the UAV.
Implementation Method 1
acquiring the object distance with the binocular mode comprises calculating the object distance using a binocular triangulation
Implementation Method 2
obtaining a disparity between two images of an object, which images are captured using an imaging device
Data Source
AI summary
A method for assisting obstacle avoidance of a mobile platform includes determining to use a detection mode from a plurality of detection modes, detecting a characteristic condition of the mobile platform with respect to an obstacle using the detection mode, and directing the mobile platform to avoid the obstacle based on the detected characteristic condition.