Adaptive Obstacle Fusion for UAV Avoidance Trajectory Control
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Solution Overview
Problem
Current flight control systems for UAVs and UASs are inadequate in detecting and avoiding obstacles, especially non-cooperative targets, as they rely on costly radar arrays and are limited by GPS accuracy and the need for transponders, failing to ensure collision avoidance with stationary and moving obstacles in varying environments.
Innovation Solution
An adaptive sense and avoid system that integrates a processor with flight controllers, multiple sensors, and databases to dynamically blend sensor data, assign weights based on vehicle and environmental conditions, and calculate avoidance trajectories, enabling the detection and navigation around obstacles regardless of their cooperative or non-cooperative nature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If radar arrays are used to detect non-cooperative obstacles, then detection capability is improved, but system cost and complexity increase significantly
Solution Approach 1:
The patent combines multiple sensor types (cooperative sensors like ADS-B/TCAS and non-cooperative sensors like radar and visual systems) into a unified sensor fusion system. This merging allows the system to leverage the strengths of each sensor type while reducing overall complexity compared to using radar arrays alone for all detection scenarios.
Solution Approach 2:
The system dynamically adjusts sensor activation and weighting based on local environmental conditions, obstacle type, and operational context. Cooperative sensors are prioritized when transponders are present, while non-cooperative sensors are activated for obstacles without transponders, optimizing detection capability while minimizing unnecessary system complexity.
2Device complexity
If GPS-based obstacle databases are used, then system cost is reduced, but measurement precision and reliability deteriorate due to GPS accuracy limitations and incomplete obstacle coverage
Solution Approach 1:
The system merges GPS-based obstacle databases with real-time sensor data from multiple sensors. This combination allows the system to use the cost-effective GPS database for general obstacle information while supplementing it with precise real-time measurements from radar and visual sensors to achieve both cost efficiency and high measurement precision.
Solution Approach 2:
The system continuously compares predicted obstacle positions from GPS databases with actual sensor detections, using feedback to correct GPS accuracy limitations. This feedback mechanism maintains measurement precision while keeping the system cost-effective by relying on the inexpensive GPS database as a baseline.
3Measurement precision
If TCAS is used for collision avoidance, then cooperative obstacle detection is improved, but adaptability to non-cooperative obstacles deteriorates
Solution Approach 1:
The system implements a universal detection framework that can handle both cooperative and non-cooperative obstacles using the same sensor fusion architecture. TCAS and ADS-B are used for cooperative targets, while radar and visual sensors provide universal detection capability for all obstacle types, making the system adaptable to any obstacle regardless of transponder presence.
Solution Approach 2:
The system adjusts its detection strategy based on the local characteristics of each obstacle. Cooperative obstacles with transponders receive higher detection priority using TCAS/ADS-B, while non-cooperative obstacles trigger activation of non-cooperative sensors. This local adaptation maintains high precision for cooperative targets while extending versatility to all obstacle types.
4Reliability
If multiple sensors are integrated with adaptive fusion, then reliability and adaptability are improved, but device complexity increases
Solution Approach 1:
The system employs dynamic sensor fusion where sensor weighting and activation are continuously adjusted based on operational conditions, obstacle type, and environmental factors. This dynamic approach improves reliability by adapting to varying scenarios while managing complexity through automated decision-making algorithms that select optimal sensor combinations in real-time.
Solution Approach 2:
The system changes operational parameters such as sensor activation states, data fusion weights, and detection thresholds based on environmental conditions and obstacle characteristics. These parameter changes enable the system to maintain high reliability across diverse scenarios while managing complexity through parameter optimization rather than structural complexity.
Data Source
AI summary
An adaptive sense and avoid system for use with an aerial vehicle in an environment, the adaptive sense and avoid system comprising a processor, a plurality of sensors, an obstacle detection circuit, and an avoidance trajectory circuit. The processor may be operatively coupled with a flight controller and a memory device, wherein the memory device comprises one or more databases. The plurality of sensors generates sensor data reflecting a position of an obstacle in the environment. The obstacle detection circuit, which is operatively coupled to the processor and the plurality of sensors, may be configured to identify obstacles based at least in part on the sensor data and to generate obstacle information that reflects a best estimate of a position of the obstacle in the environment. The obstacle detection circuit is configured to weigh the sensor data as a function of the aerial vehicle's state and its environment. The avoidance trajectory circuit, which is operatively coupled to the obstacle detection circuit and the processor, may be configured to calculate trajectory data as a function of the obstacle information and information from the one or more databases. The obstacle detection circuit is configured to communicate the trajectory data to the flight controller.