A Fuzzy Guidance System for Rendezvous and Pursuit of Moving Targets

This article presents the development of a fuzzy guidance system (FGS) for unmanned aerial vehicles capable of pursuing and performing rendezvous with static and mobile targets. The system is designed to allow the vehicle to approach a maneuvering target from a desired direction of arrival and to terminate the rendezvous at a constant distance from the target. In order to perform a rendezvous with a maneuvering target, the desired direction of arrival is adjusted over time to always approach the target from behind, so that the aircraft and target velocity vectors become aligned. The proposed guidance system assumes the presence of an autopilot and uses a set of Takagi–Sugeno fuzzy controllers to generate the orientation and speed references for the velocity and heading control loops, given the relative position and velocity between the aircraft and the target. The FGS treats the target as a mobile waypoint in a 4-D space (position in 2-dimensions, desired crossing heading and speed) and guides the aircraft on suitable trajectories towards the target. Only when the vehicle is close enough to the rendezvous point, the guidance law is complemented with an additional linear controller to manage the terminal formation keeping phase. The capabilities of the proposed rendezvous-FGS are verified in simulation on both maneuvering and non-maneuvering targets. Finally, experimental results using a multi-rotor aerial system are presented for both fixed and accelerating targets

Real time Optimal Allocation for I-AUV with Interacting Thrusters

Energy saving is a relevant issue for battery powered Intervention Autonomous Underwater Vehicles which are designed for both short and long-range mission. The energy consumption of an I-AUV is affected by several effects like hydrodynamics, onboard electronics and thrusters cross-couplings. I-AUVs are usually over-actuated system, thus the actuator's interaction takes relevance and should take part within the energy saving process. In this paper the authors present a study on optimal control allocation that aims at considering the interactions between the propellers of an over actuated vehicle where usually two or more thrusters can interfere each other resulting in reduction of the allocation efficiency. The paper presents the mathematical formulation for interacting propeller considering the wake effect and proposes to dynamically adjust the control allocation matrix in order to obtain a cost effective control allocation without modifying the control layer. The existence of a minimum in the energy consumption during the cruising task in function of the parametric control allocation matrix is proved numerically. Thus a perturbation-based extremum seeking approach is used in order to dynamically adapt the parametric allocation matrix and seek the optimal allocation setpoint without explicit knowledge of the real coupling

A hybrid approach to detection and tracking of unmanned aerial vehicles

This paper proposes a novel, hybrid vision-based system to autonomously detect and track a specific moving target, in our case an evader UAV (Unmanned Aerial Vehicles), with a moving camera. The framework is based on a detection stage which exploits a Faster Region-based Convolutional Neural Network (Faster R-CNN) designed to detect the Region Of Interest (ROI) associated to the UAV’s position in the image plane. The moving target is tracked by using an Optical Flow-based tracking system and a Kalman Filter is used to give temporal consistency between consecutive measurements. The tracking system is designed to be able to achieve real-time image processing on embedded systems, for this reason a lag compensation algorithm for the delay due to the Faster R-CNN computation time is implemented. Algorithm’s performance is evaluated by computing the error between the true UAV position in the image plane and the estimated position resulting from the tracking system

Simultaneous tracking of multiple drones using convolutional neural networks and optical flow

The aim of this paper is to implement a multi-object tracker for detecting, discriminating and following multiple drones on the 2D image plane. The main system consists in a neural network trained for detecting drones on the image plane. Once the drones are identified, their Region Of Interest information are sent to the Kalman filter based tracking system in order to compute position estimates. Each detected drone is given an ID using the Global Nearest Neighbor and Auction Algorithms, then the tracking system employs a set of Kalman filters and each measure is assigned to the respective estimate/filter using the unique ID. In order to reduce the effects of the neural network computation (e.g detection) time, every position estimate is corrected using Optical Flow. Results are evaluated through MOT (Multi-Object Tracking) Metrics, aiming to demonstrate the benefits of implementing Optical Flow algorithms for building a robust multi-tracking system

Development of the Guidance Navigation and Control System of the Folaga AUV for Autonomous Acoustic Surveys in the WiMUST Project

This paper deals with the development of the Guidance Navigation and Control (GNC) system of the Autonomous Underwater Vehicle (AUV) used for acoustic surveys in the WiMUST projects. By exploiting the fact that the vehicle hull has a modular structure, a specific payload module was realized containing a single board computer, two acoustic modems, the electronic boards for signal conditioning and data storage of seismic data, and the mechanical interface for the streamer, the array of hydrophones that constitutes the main mission payload. Then, by using the Robot Operating System (ROS) the mission control system was implemented in the single board computer inside the additional payload segment. Motion control for the AUV was realized designing controllers for surge speed, heading and depth. Design of the depth controller represented one of the major challenges mainly because towing the streamer heavily affects the vehicle dynamics when underwater. Thus, a specific fuzzy-PID control system was implemented and tested. This one, together with the surge speed and the heading controller are discussed in this paper. Finally, selected experimental results from sea trials are discussed to prove the effectiveness of the presented GNC system

Overview of the Main Activities of Hyperhealth Project - Results of Hyperspectral Prisma Data Exploitation

This paper provides an overview of the main activities and results of HYPERHEALTH project (funded by the Italian Space Agency). Specific focus of this paper is hyperspectral PRISMA data exploitation, mostly as regards PRISMA-based atmospheric constituent estimation and allergenic vegetation monitoring

Hyperhealth - Environmental Impact Assessment On Human Health: Advanced Methods For Hyperspectral Prisma Data Exploitation

HYPERHEALTH project is co-funded by Italian Space Agency (ASI) in the framework of the "PRISMA Scienza" program. The program supports R&D projects proposed by experts in hyperspectral remote sensing sector from national public research institutions to industries, also in the framework of international partnerships. The aim is designing, developing and testing innovative methods, techniques and algorithms for exploitation of hyperspectral data, with reliable perspectives as to engineering and pre-operational development, thus contributing to the improvement of socio-economic benefits of the end-user community. This paper outlines HYPERHEALTH main goals and activities