Abstract:The integration of Unmanned Aerial Vehicles (UAVs) 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 optimization based approaches under 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 manoeuvre. 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 simplified 4 Degree of Freedom (4DoF) kinematic and dynamic model of a UAV, the path planning and collision avoidance algorithms are developed in 3D. 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 optimisation algorithms are applied and compared in terms of the reliability and efficiency

Abstract:The integration of Unmanned Aerial Vehicles (UAVs) 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 optimization based approaches under 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 manoeuvre. 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 simplified 4 Degree of Freedom (4DoF) kinematic and dynamic model of a UAV, the path planning and collision avoidance algorithms are developed in 3D. 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 optimisation algorithms are applied and compared in terms of the reliability and efficiency

SUMMARYThis paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, and illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncertainties in physical parameters, sensor measurements, and even the model structure of the robot are taken into account in the worst-case analysis. The minimum distance to a moving obstacle is considered as an objective function in automatic search process. It is demonstrated that a local nonlinear optimization method may not be adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. The Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches.

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