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

Failure boundary estimation for lateral collision avoidance manoeuvres

This paper proposes a method for predicting the point at which a simple lateral collision avoidance manoeuvre fails. It starts by defining the kinematic failure boundary for a range of conflict geometries and velocities. This relies on the assumption that the ownship aircraft is able to turn instantaneously. The dynamics of the ownship aircraft are then introduced in the form of a constant rate turn model. With knowledge of the kinematic boundary, two optimisation algorithms are used to estimate the location of the real failure boundary. A higher fidelity simulation environment is used to compare the boundary predictions. The shape of the failure boundary is found to be heavily connected to the kinematic boundary prediction. Some encounters where the ownship aircraft is travelling slower than the intruder were found to have large failure boundaries. The optimisation method is shown to perform well, and with alterations to the turn model, its accuracy can be improved. The paper finishes by demonstrating how the failure boundary is used to determine accurate collision avoidance logic. This is expected to significantly reduce the size and complexity of the verification problem

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

Optimization-Based Safety Analysis of Obstacle Avoidance Systems for Unmanned Aerial Vehicles

This article was published in the Journal of Intelligent and Robotic Systems [© Springer Science+Business Media B.V.]. The definitive version is available at: http://link.springer.com/article/10.1007%2Fs10846-011-9586-0?LI=true#. The original publication is available at www.springerlink.com.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

Supporting Validation of UAV Sense-and-Avoid Algorithms with Agent-Based Simulation and Evolutionary Search

A Sense-and-Avoid (SAA) capability is required for the safe integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace. Given their safety-critical nature, SAA algorithms must undergo rigorous verification and validation before deployment. The validation of UAV SAA algorithms requires identifying challenging situations that the algorithms have difficulties in handling. By building on ideas from Search-Based Software Testing, this thesis proposes an evolutionary-search-based approach that automatically identifies such situations to support the validation of SAA algorithms. Specifically, in the proposed approach, the behaviours of UAVs under the control of selected SAA algorithms are examined with agent-based simulations. Evolutionary search is used to guide the simulations to focus on increasingly challenging situations in a large search space defined by (the variations of) parameters that configure the simulations. An open-source tool has been developed to support the proposed approach so that the process can be partially automated. Positive results were achieved in a preliminary evaluation of the proposed approach using a simple two-dimensional SAA algorithm. The proposed approach was then further demonstrated and evaluated using two case studies, applying it to a prototype of an industry-level UAV collision avoidance algorithm (specifically, ACAS XU) and a multi-UAV conflict resolution algorithm (specifically, ORCA-3D). In the case studies, the proposed evolutionary-search-based approach was empirically compared with some plausible rivals (specifically, random-search-based approaches and a deterministic-global-search-based approach). The results show that the proposed approach can identify the required challenging situations more effectively and efficiently than the random-search-based approaches. The results also show that even though the proposed approach is a little less competitive than the deterministic-global-search-based approach in terms of effectiveness in relatively easy cases, it is more effective and efficient in more difficult cases, especially when the objective function becomes highly discontinuous. Thus, the proposed evolutionary-search-based approach has the potential to be used for supporting the validation of UAV SAA algorithms although it is not possible to show that it is the best approach