A unified approach to cooperative and non-cooperative sense-and-avoid

Cooperative and non-cooperative Sense-and-Avoid (SAA) capabilities are key enablers for Unmanned Aircraft Vehicle (UAV) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative SAA sensor/system technologies for small-to-medium size UAV are identified and the associated multi-sensor data fusion techniques are introduced. A reference SAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting and sorting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the SAA system processes, the key mathematical models associated with both non-cooperative and cooperative SAA functions are presented. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are described. Based on these mathematical models, the SAA Unified Method (SUM) for cooperative and non-cooperative SAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UAV) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy SAA capability, which provides a pathway for manned/unmanned aircraft coexistence in all classes of airspace

Avionics sensor fusion for small size unmanned aircraft Sense-and-Avoid

Cooperative and non-cooperative Sense-and-Avoid (SAA) systems are key enablers for Unmanned Aircraft (UA) to routinely access non-segregated airspace. In this paper some state-of-the-art cooperative and non-cooperative sensor and system technologies are investigated for small size UA applications, and the associated multisensor data fusion techniques are discussed. Non-cooperative sensors including both passive and active Forward Looking Sensors (FLS) and cooperative systems including Traffic Collision Avoidance System (TCAS), Automatic Dependent Surveillance - Broadcast (ADS-B) system and/or Mode C transponders are part of the proposed SAA architecture. After introducing the SAA system processes, the key mathematical models for data fusion are presented. The Interacting Multiple Model (IMM) algorithm is used to estimate the state vector of the intruders and this is propagated to predict the future trajectories using a probabilistic model. Adopting these mathematical models, conflict detection and resolution strategies for both cooperative and un-cooperative intruders are identified. Additionally, a detailed error analysis is performed to determine the overall uncertainty volume in the airspace surrounding the intruder tracks. This is accomplished by considering both the navigation and the tracking errors affecting the measurements and translating them to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Detailed simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UA) and various intruder platforms, including large transport aircraft and other UA. Results show that the required safe separation distance is always maintained when the SAA process is performed from ranges in excess of 500 metres

Multirotor UAS Sense and Avoid with Sensor Fusion

In this thesis, the key concepts of independent autonomous Unmanned Aircraft Systems (UAS) are explored including obstacle detection, dynamic obstacle state estimation, and avoidance strategy. This area is explored in pursuit of determining the viability of UAS Sense and Avoid (SAA) in static and dynamic operational environments. This exploration is driven by dynamic simulation and post-processing of real-world data. A sensor suite comprised of a 3D Light Detection and Ranging (LIDAR) sensor, visual camera, and 9 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) was found to be beneficial to autonomous UAS SAA in urban environments. Promising results are based on to the broadening of available information about a dynamic or fixed obstacle via pixel-level LIDAR point cloud fusion and the combination of inertial measurements and LIDAR point clouds for localization purposes. However, there is still a significant amount of development required to optimize a data fusion method and SAA guidance method

Vision based strategies for implementing Sense and Avoid capabilities onboard Unmanned Aerial Systems

Current research activities are worked out to develop fully autonomous unmanned platform systems, provided with Sense and Avoid technologies in order to achieve the access to the National Airspace System (NAS), flying with manned airplanes. The TECVOl project is set in this framework, aiming at developing an autonomous prototypal Unmanned Aerial Vehicle which performs Detect Sense and Avoid functionalities, by means of an integrated sensors package, composed by a pulsed radar and four electro-optical cameras, two visible and two Infra-Red. This project is carried out by the Italian Aerospace Research Center in collaboration with the Department of Aerospace Engineering of the University of Naples “Federico II”, which has been involved in the developing of the Obstacle Detection and IDentification system. Thus, this thesis concerns the image processing technique customized for the Sense and Avoid applications in the TECVOL project, where the EO system has an auxiliary role to radar, which is the main sensor. In particular, the panchromatic camera performs the aiding function of object detection, in order to increase accuracy and data rate performance of radar system. Therefore, the thesis describes the implemented steps to evaluate the most suitable panchromatic camera image processing technique for our applications, the test strategies adopted to study its performance and the analysis conducted to optimize it in terms of false alarms, missed detections and detection range. Finally, results from the tests will be explained, and they will demonstrate that the Electro-Optical sensor is beneficial to the overall Detect Sense and Avoid system; in fact it is able to improve upon it, in terms of object detection and tracking performance

Collision Avoidance and Navigation of UAS Using Vision-Based Proportional Navigation

Electro-optical devices have received considerable interest due to their light weight, low cost, and low algorithm requirements with respect to computational power. In this thesis, vision-based guidance laws are developed to provide sense and avoid capabilities for unmanned aerial vehicles (UAVs) operating in complex environments with multiple static and dynamic collision threats. These collision avoidance guidance laws are based on the principle of proportional navigation (Pro-Nav), which states that a UAV is on a collision course with another vehicle or object if the line-of-sight (LOS) angles to the object remain constant. The guidance laws are designed for use with monocular electro-optical devices, which provide information on the LOS angles to potential collision threats, but not the range. The development of these guidance laws propagates from an investigation into numerous methods of Pro-Nav based guidance, including the use of LOS rate thresholding, avoidance of the most imminent threat detected, and objective-based cost optimization. The collision avoidance guidance laws were applied to nonlinear, six degree-of-freedom UAV models in various simulation environments including a varying number of static and dynamic obstacles. A final form of the avoidance law, determined from these simulation studies, was applied to a small-scale UAV model flying through a virtual urban environment, which utilizes camera-in-the-loop simulation techniques. The final results of these studies showed that the most effective approach was to implement a cost function-based avoidance law that includes a term based on the Pro-Nav intercept heading for a desired waypoint and avoidance terms for all obstacles in view that pose a collision threat. Obstacle avoidance headings in the cost function are based on the difference in the obstacle LOS rates from the magnitude of the minimum safe LOS rate. When applied to UAV simulations in a virtual urban environment, this guidance law provided successful avoidance for the case of a single building, maintained a safe heading through an urban canyon, and determined the safest path through a complex urban layout. For the case of the complex urban layout, a single collision during flight occurred due to a lack of visual feature points to contribute to the avoidance law calculation

LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid

The demand for reliable obstacle warning and avoidance capabilities to ensure safe low-level flight operations has led to the development of various practical systems suitable for fixed and rotary wing aircraft. State-of-the-art Light Detection and Ranging (LIDAR) technology employing eye-safe laser sources, advanced electro-optics and mechanical beam-steering components delivers the highest angular resolution and accuracy performances in a wide range of operational conditions. LIDAR Obstacle Warning and Avoidance System (LOWAS) is thus becoming a mature technology with several potential applications to manned and unmanned aircraft. This paper addresses specifically its employment in Unmanned Aircraft Systems (UAS) Sense-and-Avoid (SAA). Small-to-medium size Unmanned Aerial Vehicles (UAVs) are particularly targeted since they are very frequently operated in proximity of the ground and the possibility of a collision is further aggravated by the very limited see-and-avoid capabilities of the remote pilot. After a brief description of the system architecture, mathematical models and algorithms for avoidance trajectory generation are provided. Key aspects of the Human Machine Interface and Interaction (HMI2) design for the UAS obstacle avoidance system are also addressed. Additionally, a comprehensive simulation case study of the avoidance trajectory generation algorithms is presented. It is concluded that LOWAS obstacle detection and trajectory optimisation algorithms can ensure a safe avoidance of all classes of obstacles (i.e., wire, extended and point objects) in a wide range of weather and geometric conditions, providing a pathway for possible integration of this technology into future UAS SAA architectures

Aircraft Detection and Tracking Using UAV-Mounted Vision System

For unmanned aerial vehicles (UAVs) to operate safely in the national airspace where non-collaborating flying objects, such as general aviation (GA) aircraft without automatic dependent surveillance-broadcast (ADS-B), exist, the UAVs\u27 capability of “seeing these objects is especially important. This “seeing , or sensing, can be implemented via various means, such as Radar or Lidar. Here we consider using cameras mounted on UAVs only, which has the advantage of light weight and low power. For the visual system to work well, it is required that the camera-based sensing capability should be at the level equal to or exceeding that of human pilots. This thesis deals with two basic issues/challenges of the camera-based sensing of flying objects. The first one is the stabilization of the shaky videos taken on the UAVs due to vibrations at different locations where the cameras are mounted. In the thesis, we consider several algorithms, including Kalman filters and particle filters, for stabilization. We provide detailed theoretical discussions of these filters as well as their implementations. The second one is reliable detection and tracking of aircraft using image processing algorithms. We combine morphological processing and dynamic programming to accomplish good results under different situations. The performance evaluation of different image processing algorithms is accomplished using synthetic and recorded data