This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces

This paper provides an integrated overview of the three-year effort to design and implement a prototypical SAA system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (Electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The approach leverages advanced computer vision algorithms and an Extended Kalman Filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive manoeuvre computation. Initially, this integrated system has been rigorously tested by means of a sophisticated simulation environment that replicates real-world conditions, including the generation of realistic artificial scenery to evaluate the closed loop interaction between sense and avoid modules in a Hardware-in-the-Loop (HIL) setup. Preliminary results demonstrate the system’s effectiveness in real-time detection and avoidance of non-cooperative obstacles, providing high computational efficiency and responsiveness while ensuring compliance of UAV aero mechanical constraints and safety constraints in terms of minimum distance requirements. At a later stage of the project, the effectiveness of the integrated system was evaluated by means of a preliminary flight test campaign conducted at the Lucca-Tassignano airport in Italy. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, contributing significantly to the safety of flight in non-segregated airspaces.

The paper deals with the development of a model-based optimization framework for the design of power management strategies of a lightweight compound helicopter UAV equipped with hybrid propulsion, with focus on the transition manoeuvres from cruise to hover. A high-fidelity multi-physics model of the hybrid propulsion system, composed of a combustion-engine connected to a nose propeller for forward flight phases, and two permanent magnets synchronous machines driving variable-pitch main and tail rotors to provide control during vertical flight phases, is firstly presented. In the model, the aerodynamic loads are obtained by applying an advanced BEMT-based code, including post-stall, tip-loss and low-Reynolds corrections, as well as QPROP-derived induced velocities. The dynamics of the electric machines is simulated via equivalent bi-phase model taking account of the Field-Oriented Control technique used for the three-phase electronic commutation. As a first step of the dynamic optimization process, a so-called fixed-point optimization is obtained, by finding the speeds and blade pitch angles of the rotors that minimize the steady-state electrical power absorption in vertical flight phases. To compare the optimization results with a baseline, a reference manoeuvre is then defined, in which the rotors speeds are ramped from standstill to the optimal fixed-point values, while the blade pitch angles are dynamically adjusted to ensure the UAV vertical equilibrium. Subsequently, a constrained optimization problem is applied by using the high-fidelity model, determining the main rotor commands that minimize the power consumption during transition. Finally, the robustness of the system performances against parameter uncertainties is statistically characterized via Monte Carlo simulations. The results demonstrate the effectiveness of the proposed strategies in reducing the energy consumption and in improving the overall performances with respect to a baseline manoeuvre, highlighting the effectiveness of model-based optimization for the power management design of novel UAV platforms.

The present work illustrates the results obtained at the conclusion of the three-year project TERSA (Tecnologie Elettriche e Radar per Sapr Autonomi), involving the aerospace section of the Dept. of Civil and Industrial Engineering (DICI) of the University of Pisa and its industrial partners. The project aimed at the design and development of a fully autonomous Sense and Avoid (SAA) prototype system, based on data fusion between optical and radar sensors data, for a tactical lightweight surveillance UAV (MTOW<25Kg). The problem of non-cooperative collision avoidance is well known in literature and is currently a central theme of investigation within the aeronautical industry, considering the growing UAV traffic and the consequent need to employ autonomous self-separation technologies in the market. Several past works have investigated the most varied solutions for the Sense problem utilizing optical, acoustic, electro-magnetic signals or a combination of the previous. Likewise, the Avoidance problem has been successfully tackled in literature by means of a wide variety of different approaches ranging from rule-based methods, strategies based on game theory, force field methods, optimization frameworks leveraging genetic algorithms and nonlinear programming techniques and geometric methods. Yet, to the knowledge of the authors, no previous work found in literature has successfully demonstrated and validated the real-time simultaneous interaction of both sense and avoid functionalities within a highly integrated simulation environment. The present work describes the implementation of a complex, nonlinear, simulation environment conceived in order to perform real-time, Hardware-in-the-Loop (HIL), testing of the effective cooperation between sense and the avoid algorithms constituting the core of the SAA system developed within the context of the project. The system effectiveness has been validated by means of complex dynamic simulations, comprising an accurate, fully nonlinear, flight mechanic model of the aircraft, a graphic rendering engine of the scene, proper video capture and transmission pipelines, computer vision algorithms and collision avoidance logics running on the target hardware (Nvidia Jetson Nano) and tailored noise resilient data fusion algorithms. Results show the effectiveness of the system in detecting impending collisions and performing last-resort resolution manoeuvres with high computational efficiency and update frequencies compatible with real world applications in Unmanned Aircraft Systems (UAS).