Adaptive Obstacle Sensing for UAV Avoidance Trajectory Control
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Solution Overview
Problem
Current flight control systems for UAVs are inadequate in detecting and avoiding obstacles, especially non-cooperative targets like birds and other air traffic without transponders, due to limitations in existing obstacle databases and varying GPS accuracy, which can lead to collisions.
Innovation Solution
An adaptive sense and avoid system that integrates a processor, multiple sensors, an obstacle detection circuit, and an avoidance trajectory circuit to dynamically blend sensor data from various sources, including cooperative and non-cooperative sensors, and adjust sensor modes based on the UAV's state and environment, to generate accurate obstacle information and calculate avoidance trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional obstacle detection methods using obstacle databases and GPS are used, then the system is simple to operate, but the measurement precision and reliability of obstacle detection are insufficient
Solution Approach 1:
The patent combines multiple detection methods including cooperative sensors (ADS-B, TCAS), non-cooperative sensors (radar, visual systems), and obstacle databases into a unified sensor fusion system. This integration allows the system to leverage the strengths of each individual method while compensating for their weaknesses, thereby improving overall detection precision without relying on a single complex system.
Solution Approach 2:
The system employs multi-functional sensors that can detect both cooperative targets (with transponders) and non-cooperative targets (without transponders) using the same hardware platform. The sensor fusion architecture enables a single system to perform multiple detection functions across different operational scenarios, reducing the need for separate specialized systems.
2Reliability
If TCAS is used for cooperative aircraft detection, then the detection reliability is improved for transponder-equipped aircraft, but the system cannot detect non-cooperative obstacles
Solution Approach 1:
The detection system is segmented into specialized subsystems: one dedicated to cooperative target detection (TCAS, ADS-B) and another for non-cooperative target detection (radar, visual systems). Each subsystem is optimized for its specific function, and their results are integrated through sensor fusion to achieve comprehensive detection coverage across all target types.
Solution Approach 2:
The sensor fusion algorithm acts as an intermediary that combines detection data from both cooperative and non-cooperative sensor systems. It reconciles the information from different sensor types and sources, producing a unified obstacle detection result that leverages the reliability of TCAS for cooperative targets while incorporating radar and visual data for non-cooperative targets.
3Measurement precision
If radar arrays are used to detect non-cooperative obstacles, then the detection capability is improved, but the system becomes too costly for UAV applications
Solution Approach 1:
The system replaces expensive, heavy radar arrays with more cost-effective sensors such as small radars, camera-based visual systems, and other detection sensors suitable for UAVs. These lighter, cheaper sensors are optimized for the specific detection needs of non-cooperative targets in UAV operational environments, providing adequate detection capability without the prohibitive cost of military-grade radar arrays.
Solution Approach 2:
The system changes the operational parameters of sensors to optimize performance for non-cooperative target detection. By adjusting detection thresholds, integration times, and sensor modes based on environmental conditions and target characteristics, the system achieves effective detection capability with less expensive sensors rather than relying solely on high-power radar arrays.
4Ease of operation
If GPS-based obstacle avoidance is used, then the system is easy to implement, but the measurement precision varies widely across different environments and altitudes
Solution Approach 1:
The system uses feedback from multiple sensor sources to continuously monitor and correct GPS position estimates. When GPS accuracy degrades in certain environments or altitudes, the sensor fusion algorithm incorporates corrective information from other sensors (radar, visual systems, inertial sensors) to maintain accurate obstacle detection and avoidance, thereby preserving both ease of operation and measurement precision.
Data Source
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Figure 1c
Figure 2a
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.