Abstract-In this paper we present a collision avoidance system based on visual detection. Our hardware consists of a Hummingbird quadrotor equipped with a large red marker with two built-in fish-eye cameras. Fusion of the measurements from the two cameras is done using a Gaussian-mixture probability hypothesis density filter, which allows for tracking several aircrafts at the same time. Our collision avoidance algorithm is based on navigation functions designed to cope with cameras characterized by limited field of view. Its mathematical correctness has been proven in a former paper [1]. The collision avoidance maneuver is performed without the vehicles explicitly exchanging information via communication but instead relying solely on on-board sensors. Our system has been validated in an indoor space with four different collision scenarios. Trajectory data was recorded with an external motion capture system and demonstrate good robustness against sensing noise.

Abstract-This paper proposes a novel approach for collision avoidance between Unmanned Aerial Vehicles (UAVs) with limited range and field of view sensors. The algorithm is designed for unicycle vehicles that need to fly above a specific minimal speed to maintain flight. It uses a navigation function approach when the UAV is clear from conflict and smoothly switches to a collision avoidance maneuver when other UAVs are encountered. We show that the proposed avoidance algorithm can ensure a collision-free path. We also carry out a throughout quantitative analysis of the algorithm singularities and propose heuristic recipes for avoiding deadlock situations. Simulations are performed to show the effectiveness of the algorithm.

Abstract-We propose an approach for on-line detection of small Unmanned Aerial Vehicles (UAVs) and estimation of their relative positions and velocities in the 3D environment from a single moving camera in the context of sense and avoid systems. This problem is challenging both from a detection point of view, as there are no markers on the targets available, and from a tracking perspective, due to misdetection and false positives. Furthermore, the methods need to be computationally light, despite the complexity of computer vision algorithms, to be used on UAVs with limited payload.To address these issues we propose a multi-staged framework that incorporates fast object detection using an AdaBoost-based approach, coupled with an on-line visual-based tracking algorithm and a recent sensor fusion and state estimation method. Our framework allows for achieving real-time performance with accurate object detection and tracking without any need of markers and customized, high-performing hardware resources.

Abstract-Unmanned Aerial Vehicles (UAVs) are becoming a significant field of research with numerous applications, ranging from mapping to surveillance. New applications, such as aerial delivery of goods, are expected to appear in the next years and will require more and more autonomy from UAVs. One challenge preventing UAVs from being fully autonomous is their current limitations in handling potential collisions among multiple vehicles. This paper presents a collision avoidance algorithm for fixed-wing UAVs navigating in a three dimensional space. It satisfies limited field of view constraints that stem from the use of a single camera system as sensing device. The proposed algorithm uses potential fields to both navigate and avoid obstacles. To guarantee collision avoidance, the algorithm is enhanced with a turning behavior that allows for ensuring the safety of the method. Simulations are performed to show the effectiveness of the proposed algorithm.