Collision avoidance, in particular between robots, is an important component for autonomous robots. It is a necessary component in numerous applications such as humanrobot interaction, automotive or unmanned aerial vehicles. While many collision avoidance algorithms take into account actuation constraints, only a few consider sensing limitations. In this paper, we present a reciprocal collision avoidance algorithm based on the velocity obstacle approach that guarantees collision-free maneuvers even when the robots are only capable to sense their environment within a limited Field Of View (FOV). We also present the challenges associated to sensors with limited FOV, show the conditions under which maneuvering can be safely done, and the modifications that a velocity obstacle approach requires to satisfy such conditions. We provide simulations and real robot experiments to validate our approach.

The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 °C over a 16 m2 area ranging in value from 25-65 °C.

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.

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.