Simulation of an enhanced TCAS 2 system in operation

Described is a computer simulation of a Boeing 737 aircraft equipped with an enhanced Traffic and Collision Avoidance System (TCAS II). In particular, an algorithm is developed which permits the computer simulation of the tracking of a target airplane by a Boeing 373 which has a TCAS II array mounted on top of its fuselage. This algorithm has four main components: namely, the target path, the noise source, the alpha-beta filter, and threat detection. The implementation of each of these four components is described. Furthermore, the areas where the present algorithm needs to be improved are also mentioned

Analyzing helicopter evasive maneuver effectiveness against rocket-propelled grenades

It has long been acknowledged that military helicopters are vulnerable to ground-launched threats, in particular, the RPG-7 rocket-propelled grenade. Current helicopter threat mitigation strategies rely on a combination of operational tactics and selectively placed armor plating, which can help to mitigate but not entirely remove the threat. However, in recent years, a number of active protection systems designed to protect land-based vehicles from rocket and missile fire have been developed. These systems all use a sensor suite to detect, track, and predict the threat trajectory, which is then employed in the computation of an intercept trajectory for a defensive kill mechanism. Although a complete active protection system in its current form is unsuitable for helicopters, in this paper, it is assumed that the active protection system’s track and threat trajectory prediction subsystem could be used offline as a tool to develop tactics and techniques to counter the threat from rocket-propelled grenade attacks. It is further proposed that such a maneuver can be found by solving a pursuit–evasion differential game. Because the first stage in solving this problem is developing the capability to evaluate the game, nonlinear dynamic and spatial models for a helicopter, RPG-7 round, and gunner, and evasion strategies were developed and integrated into a new simulation engine. Analysis of the results from representative vignettes demonstrates that the simulation yields the value of the engagement pursuit–evasion game. It is also shown that, in the majority of cases, survivability can be significantly improved by performing an appropriate evasive maneuver. Consequently, this simulation may be used as an important tool for both designing and evaluating evasive tactics and is the first step in designing a maneuver-based active protection system, leading to improved rotorcraft survivability

Optimisation-based verification process of obstacle avoidance systems for unmanned vehicles

This thesis deals with safety verification analysis of collision avoidance systems for unmanned vehicles. The safety of the vehicle is dependent on collision avoidance algorithms and associated control laws, and it must be proven that the collision avoidance algorithms and controllers are functioning correctly in all nominal conditions, various failure conditions and in the presence of possible variations in the vehicle and operational environment. The current widely used exhaustive search based approaches are not suitable for safety analysis of autonomous vehicles due to the large number of possible variations and the complexity of algorithms and the systems. To address this topic, a new optimisation-based verification method is developed to verify the safety of collision avoidance systems. The proposed verification method formulates the worst case analysis problem arising the verification of collision avoidance systems into an optimisation problem and employs optimisation algorithms to automatically search the worst cases. Minimum distance to the obstacle during the collision avoidance manoeuvre is defined as the objective function of the optimisation problem, and realistic simulation consisting of the detailed vehicle dynamics, the operational environment, the collision avoidance algorithm and low level control laws is embedded in the optimisation process. This enables the verification process to take into account the parameters variations in the vehicle, the change of the environment, the uncertainties in sensors, and in particular the mismatching between model used for developing the collision avoidance algorithms and the real vehicle. It is shown that the resultant simulation based optimisation problem is non-convex and there might be many local optima. To illustrate and investigate the proposed optimisation based verification process, the potential field method and decision making collision avoidance method are chosen as an obstacle avoidance candidate technique for verification study. Five benchmark case studies are investigated in this thesis: static obstacle avoidance system of a simple unicycle robot, moving obstacle avoidance system for a Pioneer 3DX robot, and a 6 Degrees of Freedom fixed wing Unmanned Aerial Vehicle with static and moving collision avoidance algorithms. It is proven that although a local optimisation method for nonlinear optimisation is quite efficient, it is not able to find the most dangerous situation. Results in this thesis show that, among all the global optimisation methods that have been investigated, the DIviding RECTangle method provides most promising performance for verification of collision avoidance functions in terms of guaranteed capability in searching worst scenarios

Optimization-based safety analysis of obstacle avoidance systems for unmanned aerial vehicles

The integration of Unmanned Aerial Vehicles (UAVs) in airspace requires new methods to certify collision avoidance systems. This paper presents a safety clearance process for obstacle avoidance systems, where worst case analysis is performed using simulation based optimization in the presence of all possible parameter variations. The clearance criterion for the UAV obstacle avoidance system is defined as the minimum distance from the aircraft to the obstacle during the collision avoidance maneuver. Local and global optimization based verification processes are developed to automatically search the worst combinations of the parameters and the worst-case distance between the UAV and an obstacle under all possible variations and uncertainties. Based on a 6 Degree of Freedom (6DoF) kinematic and dynamic model of a UAV, the path planning and collision avoidance algorithms are developed in 3D space. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is a simple and widely used method. Different optimization algorithms are applied and compared in terms of the reliability and efficiency

Obstacle detection and collision avoidance method based on optical systems

The development of a new collision avoidance method, which can detect and calculate the necessary changes to prevent imminent accident, is the focal interest of this work. In aviation, the risk of collision is a delicate and important subject, which merits the right approach. With the continuing growth of air traffic and the introduction of RPASs (Remotely Piloted Aircraft System), it is necessary to find better solutions and develop new systems to keep the control of the airspace. In this work, the main objective is to obtain a complete and functional computational algorithm, which could be included in an obstacle detection and avoidance system. Its unique feature of optical detection makes it mostly appropriated for RPASs. The application of Optical Techniques is mostly used in aircrafts to detect objects under them [1] or even to prevent a collision with terrain [2]. Some technologies also use optic flow sensors to detect and prevent collisions [3, 4]. In this case, the optical system will be used to detect obstacles in front of the aircraft. The detection of an obstacle will be performed by the two infrared cameras strategically positioned in the aircraft. The objectives to accomplish with this method are: capable of dealing with collision detection characteristics; in case of detecting a possible threat of collision, describing the safe zone as the area outside a conflict cone; assessing if the threat of collision previously detected is real; in case the danger is real, changing the aircraft’s trajectory by altering one or more flight characteristics. To achieve the most efficient method possible some theoretical methods were explored, like the Convex Hull Method, which is a simple geometrical method, and a variation method based on differential equations. With the aim of testing the algorithm in different situations, a total of six possible cases were generated. All the results showed coherence and efficiency, which confirms the success of this computational algorithm as a detection and collision avoidance method.O desenvolvimento de um novo sistema de prevenção de colisões, que consiga detetar e calcular as mudanças necessárias para prevenir um acidente iminente, é o interesse focal deste trabalho. Na aviação, o risco de colisão é um assunto delicado e importante, o qual merece a correta abordagem. Com o crescimento continuo do trafego aéreo e a introdução dos RPASs (Remotely Piloted Aircraft System), é necessário procurar melhores soluções e desenvolver novos sistemas para manter o controlo do espaço aéreo. Neste trabalho, o principal objetivo é obter um algoritmo computacional completo e funcional, o qual poderá ser incluído num sistema de deteção e evasão de obstáculos. A sua característica única de deteção ótica torna--o principalmente apropriado para RPASs. A aplicação de Técnicas Óticas é principalmente utilizada nas aeronaves para deteção de objetos debaixo destas [1] ou mesmo para prevenir uma colisão com o terreno [2]. Algumas tecnologias utilizam sensores de fluxo ótico para detetar e prevenir colisões [3, 4]. Neste caso, o sistema ótico será utilizado para detetar obstáculos à frente da aeronave. Os objetivos a realizar com este sistema são: capaz de lidar com as características de deteção de colisão; em caso de detetar uma possível ameaça de colisão, descrever a zona segura como a área fora do cone de conflito; avaliar se a ameaça de colisão é real; no caso do perigo ser real, mudar a trajetória da aeronave alterando uma ou mais característica de voo. Para obter o sistema mais eficiente possível alguns métodos teóricos foram explorados, como o método do ‘Convex Hull’, o qual é um simples método geométrico, e um método de variação com base nas equações diferenciais. Com o objetivo de testar o sistema em diferentes situações, um total de seis casos possíveis foram gerados. Todos os resultados mostraram coerência e eficácia, o que confirma o sucesso do algoritmo computacional como um sistema de deteção e evasão de colisões

Decentralized UAV guidance using modified boid algorithms

Decentralized guidance of Unoccupied Air Vehicles (UAVs) is a very challenging problem. Such technology can lead to improved safety, reduced cost, and improved mission efficiency. Only a few ideas for achieving decentralized guidance exist, the most effective being the boid algorithm. Boid algorithms are rule-based guidance methods derived from observations of animal swarms. In this paper, boid rules are used to autonomously control a group of UAVs in high-level transit simulations. This paper differs from previous work in that, as an alternative to using exponentially scaled behavior weightings, the weightings are computed off-line and scheduled according to a contingency management system. The motivation for this technique is to reduce the amount of on-line computation required by the flight system. Many modifications to the basic boid algorithm are required in order to achieve a flightworthy design. These modifications include the ability to define flight areas, limit turning maneuvers in accordance with the aircraft dynamics, and produce intelligent waypoint paths. The use of a contingency management system is also a major modification to the boid algorithm. A Simple Genetic Algorithm is used to partially optimize the behavior weightings of the boid algorithm. While a full optimization of all contingencies is not performed due to computation requirements, the framework for such a process is developed. Wolfram\u27s Matlab software is used to develop and simulate the boid guidance algorithm. The algorithm is interfaced with Cloud Cap Technology\u27s Piccolo autopilot system for Hardware-in-the-Loop simulations. These high-fidelity simulations prove this technology is both feasible and practical. They also prove the boid guidance system developed herein is suitable for comprehensive flight testing