Aerial Animal Biometrics:Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference

This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation able to autonomously find and visually identify by coat pattern individual Holstein Friesian cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft; that is, a YoloV2-based species detector, a dual-stream CNN delivering exploratory agency and an InceptionV3-based biometric LRCN for individual animal identification. We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 146.7 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error-free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind and a successful step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.Comment: Accepted 7 page manuscript to be presented at IROS 201

Bio-inspired Distributed Strain and Airflow Sensing for Small Unmanned Air Vehicle Flight Control

Flying animals such as birds, bats and insects all have extensive arrays of sensory or- gans distributed in their wings which provide them with detailed information about the airflow over their wings and the forces generated by this airflow. Using two small modified unmanned air vehicle platforms (UAVs), one with a distributed array of 12 strain gauge sensors and one with a chord-wise array of 4 pressure sensors, we have examined the dis- tribution of the strain and air pressure signals over the UAV wings in relation to flight conditions, including wind tunnel testing, indoor free flight and outdoor free flight. We have also characterised the signals provided by controlled gusts and natural turbulence. These sensors were then successfully used to control roll motions in the case of the strain sensor platform and pitch motions in the case of the pressure sensor platform. These results suggest that distributed mechanosensing and airflow sensing both offer advantages beyond traditional flight control based on rigid body state estimation using inertial sensing. These advantages include stall detection, gust alleviation and model-free measurement of aerodynamic forces. These advantages are likely to be important in the development of future aircraft with increasing numbers of degrees of freedom both through flexibility and active morphing.</p