In this article, we review the main results achieved by the research activities carried out at PRISMA Lab of the University of Naples Federico II where, for 35 years, an interdisciplinary team of experts developed robots that are ultimately useful to humans. We summarize the key contributions made in the last decade in the six research areas of dynamic manipulation and locomotion, aerial robotics, human-robot interaction, artificial intelligence and cognitive robotics, industrial robotics, and medical robotics. After a brief overview of each research field, the most significant methodologies and results are reported and discussed, highlighting their cross-disciplinary and translational aspects. Finally, the potential future research directions identified are discussed.

In the domain of maritime security and safety, the surveillance and monitoring of vessels in ports are crucial for safeguarding against potential threats and ensuring the overall integrity of port operations. Current port surveillance methods, including radar systems, CCTV networks, and AIS, exhibit limitations, necessitating innovative solutions. The integration of drone-captured imagery holds promise, particularly for detecting non-cooperative vessels that may evade traditional surveillance methods. This paper presents VESSELimg dataset, a meticulously gathered and annotated collection of drone-captured images within port environments. This dataset serves as a benchmark for training and validating deep learning models, specifically tailored for automatic vessel detection, covering diverse vessel types. The implementation of a YOLO-based deep learning model for real-time inference demonstrates the practical applicability of the dataset, underscoring its potential for enhancing security and safety measures in port environments.

Ensuring the security of strategic areas through automated surveillance is an important task in safeguarding the well-being of the populace. In some cases, these are are huge and difficult to patrol with fixed cameras. An example of such areas is represented by the ports, where vigilant oversight of entry points is imperative to thwart unauthorized vessel access and preemptively mitigate potential threats to human safety and critical infrastructure. For this reason, we present an approach to achieve this goal, leveraging Unmanned Aerial Vehicles (UAVs) capable of detecting and tracking moving targets navigating on the sea’s and ground’s surface by using standard cameras and leveraging on a combination of machine learning and model-based techniques deriving the precise geolocalization of target vessels addressing critical factors such as the effects of UAV localization uncertainty. The effectiveness of the proposed approach is demonstrated within two case studies, intended to evaluate the geolocalization system from a quantitative and qualitative point of view in both controlled and realistic testing environments.

Autonomous inspection and maintenance tasks with robots in oil and gas refineries require moving along pipelines and manipulation dexterity in cluttered environments. This paper investigates the problem of controlling a wheeled mobile manipulator endowed with a snake-like arm to inspect the structures while stabilizing the supporting pipe. A model predictive control approach stabilizes the wheeled robot on the pipe. When the wheel torques saturate, the stabilization task leverages the resulting propagating force on the wheeled robot given by the snake-like arm’s dynamics. The significant number of degrees of freedom given by the snake-like arm allows a prioritized redundancy resolution scheme with hybrid motion/force tracking to inspect the same and adjacent pipes while avoiding self-collisions and environmental impacts. Simulations in the realistic Gazebo environment validate the achieved preliminary results.

Rapid identification of motor failures holds significant importance for ensuring the safety of multi-rotor unmanned aerial vehicles. This study introduces a method for detecting and isolating motor faults in standard quadrotors, utilizing an external wrench estimator and a recurrent neural network with long short-term memory nodes. The proposed approach treats partial or complete motor failure as an external disturbance affecting the quadrotor. Consequently, the external wrench estimator trains the network to quickly discern whether the estimated wrench results from a motor fault, identifying the specific motor involved, or if it stems from unmodeled dynamics or external factors such as wind or contacts. The training and testing of this method were conducted in a simulation environment equipped with a physics engine.

Fast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing one or more rotors. We present a motor fault detection and isolation (FDI) method for multi-rotor UAVs based on an external wrench estimator and a recurrent neural network composed of long short-term memory nodes. The proposed approach considers the partial or total motor fault as an external disturbance acting on the UAV. Hence, the devised external wrench estimator trains the network to promptly understand whether the estimated wrench comes from a motor fault (also identifying the motor) or from unmodelled dynamics or external effects (i.e., wind, contacts, etc.). Training and testing have been performed in a simulation environment endowed with a physic engine, considering different UAV models operating under unknown external disturbances and unexpected motor faults. To further assess this approach’s effectiveness, we compare our method’s performance with a classical model-based technique. The collected results demonstrate the effectiveness of the proposed FDI approach.

The PlaCE project aims at investigating technologies and solutions for the eco-sustainable reuse of offshore platforms at the end of their production phase. this context, robotic mobile solutions that allow in a versatile way to monitor the activities by acquiring environmental data and parameters in the entire area interest have been explored.This paper presents a solution achieved through the development of the proof of-concept of a robotic technology concerning a hybrid Autonomous / Remotely Piloted Aircraft System (A/RPAS), hereinafter referred to as Amphibious Drone, operating in complete autonomy and having the platform as an operational base. The Amphibious Drone is developed to cover the entire area of interest surrounding the platform being con-verted, with the ability to carry the measuring instruments and probes in aerial overflight for monitoring the sea surface and the surface structures as well as ditching to deploy a sensors-equipped probe for measuring underwater parameters along the water column. A wide range of probes and instruments have been integrated into the system such as multispec-tral camera, Photosynthetically Active Radiation (PAR) probes, Conductivity -Temperature-Depth (CTD) probes, etc. for the analysis of oceanographic and biological parameters of the marine eco-system. The management of the Amphibious Drone, as regards the shelter be-tween missions, battery recharging, data exchange, required reconfigurations and missions scheduling is carried out from a specially designed Docking Station. The PlaCE project is co-funded by the European Union within the projects "PON Ricerca e Innovazione 2014-2020".

In this work, we consider a scenario in which a human operator physically interacts with a collaborative robot (CoBot) to perform shared and structured tasks. We assume that collaborative operations are formulated as hierarchical task networks to be interactively executed exploiting the human physical guidance. In this scenario, the human interventions are continuously interpreted by the robotic system in order to infer whether the human guidance is aligned or not with respect to the planned activities. The interpreted human interventions are also exploited by the robotic system to on-line adapt its cooperative behavior during the execution of the shared plan. Depending on the estimated operator intentions, the robotic system can adjust tasks or motions, while regulating the robot compliance with respect to the co-worker physical guidance. We describe the overall framework illustrating the architecture and its components. The proposed approach is demonstrated in a testing scenario consisting of a human operator that interacts with the Kuka LBR iiwa manipulator in order to perform a collaborative task. The collected results show the effectiveness of the proposed approach.

Energy grids represent a fundamental infrastructure of any country. These structures consist of many kilometres of power lines that must be periodically inspected and maintained. Among the necessary operations are installing and removing bird diverters to reduce bird strikes on power lines. These devices are intended to improve birds' detection of power lines and reduce the risk of collision. Often, the installation and removal of bird diverters from power lines is accomplished by humans operating from helicopters or directly on the power lines. Apart from the considerable cost of these operations, working in elevated environments creates human safety risks. To reduce these risks, this paper proposes a novel solution to automatize these tasks. The proposed solution is a prototype gripper that can be mounted on unmanned aerial vehicles (UAVs) and remotely operated to install or remove bird diverters. This work presents a mechatronic device and software architecture system that is experimentally evaluated in a laboratory mock-up, which consists of a manipulator equipped with the proposed tool for removing a bird diverter. Future work is needed to deploy the proposed tool on a UAV.

: This paper considers a standard quadrotor drone with a cable-suspended payload and minimal sensor configuration. A neural network estimator is proposed to perform accurate real-time payload position estimation. A novel proprioceptive feedback measurement method is proposed, and a neural network has been trained with domain randomization. The network shows accurate zero-shot estimation, even with excitations never seen by the system before. This preliminary work has been tested in a simulated environment and aims to show that only onboard inertial sensing is enough to achieve the sought task. The presented work may open new applications for drone transportation in real environments subject to several perturbations.

This paper presents a general control framework for multicopters equipped with tiltable rotors (tilting multicopters). Differently from classical flat multicopters, tilting multicopters can be fully actuated systems able to decouple position and attitude control. The proposed framework has been transparently integrated into the widely used PX4 control stack, an open-source controller for ground and aerial systems, to fully exploit its high-level interfaces and functionalities and, at the same time, simplify the creation of new devices with tilting propellers. Simulation tools have been also added to the PX4 simulation framework, based on its Software-In-The-Loop (SITL) system and a set of simulated experiments in a dynamic robotic simulator have been carried out to demonstrate the effectiveness of this system. Moreover, to demonstrate the usability of the proposed framework, initial experiments with a real platform have been carried out. The proposed control framework is accessible at the following link: https://github.com/prisma- lab/PX4_tilting_multicopters

Inspecting and maintaining industrial plants is an important and emerging field in robotics. A particular case is represented by the inspection of oil and gas refinery facilities consisting of different long pipe racks to be inspected repeatedly. This task is costly in terms of human safety and operation costs due to the high altitude location in which the pipes are placed. In this domain, we propose a visual inspection system for unmanned aerial vehicles (UAVs), allowing the autonomous tracking and navigation of the center line of the industrial pipe. The proposed approach exploits a depth sensor to generate the control data for the aerial platform and, at the same time, highlight possible pipe defects. A set of simulated and real experiments in a GPS-denied environment have been carried out to validate the visual inspection system.

Quadruped robots have garnered significant attention in recent years due to their ability to navigate through challenging terrains. Among the various environments, agriculture fields are particularly difficult for legged robots, given the variability of soil types and conditions. To address this issue, this study proposes a novel navigation strategy that utilizes ground reaction forces to calculate online artificial potential fields, which are then applied to the robot's feet to avoid low-traversability regions. The strategy also incorporates the net vector of the attractive potential field towards the goal and the repulsive field to avoid slippery regions, which dynamically adjusts the quadruped's gait. A realistic simulation environment validates the proposed navigation framework with case studies on randomly generated terrains. A comprehensive comparison with baseline navigation methods is conducted to assess the effectiveness of the proposed approach.

This chapter investigates the problem of an aerial manipulator interacting with the environment. The chapter is split into two parts. The former considers an aerial device with tilting propellers that, thanks to a super-twisting slide mode controller, can control the interaction force for inspection task purposes. The latter proposes a hardware-in-the-loop simulator for human cooperation and environmental interaction with an aerial manipulator. This part includes the mathematical background and theoretical derivation with insights into the relative stability proofs. Simulations in a highly realistic environment endowed with a physics engine and real experiments validate both the proposed approaches.

: Aerial manipulation is a rapidly emerging research field that explores the use of Unmanned Aerial Vehicles as mobile manipulators. To enable aerial manipulation, UAVs must be equipped with lightweight robotic arms capable of interacting with the environment. However, due to battery life constraints and payload limitations, these arms must be designed to be as light as possible, which restricts their ability to transport and manipulate heavy objects. In this work, we introduce a novel aerial manipulator prototype designed specifically for high payload manipulation. The arm is designed to have its center of mass as close as possible to its base, which is attached to the aerial frame. The arm incorporates a system of belts to facilitate the movement of its various joints. This paper presents the arm’s design, along with a control approach to compensate for the elasticity introduced by the belts. To showcase the system’s capabilities, we conduct two sets of experiments. Firstly, the arm is tested within a controlled laboratory environment. Secondly, we deploy an aerial robot equipped with the proposed prototype in a powerline maintenance task.

This paper presents a robust control strategy for controlling the flight of an unmanned aerial vehicle (UAV) with a passively (fixed) tilted hexarotor. The proposed controller is based on a robust extended-state observer to estimate and reject internal dynamics and external disturbances at runtime. Both the stability and convergence of the observer are proved using Lyapunov-based perturbation theory and an ultimate bound approach. Such a controller is implemented within a highly realistic simulation environment that includes physics motors, showing an almost identical behavior to that of a real UAV. The controller was tested for flying under normal conditions and in the presence of different types of disturbances, showing successful results. Furthermore, the proposed control system was compared with another robust control approach, and it presented a better performance regarding the attenuation of the error signals.

The significant advances in last decade in the research and technology of multi-rotor design, modeling and control, supported by the increasing variety of commercially available platforms, components and manufacturers, have facilitated a rise in the novel applications of aerial robots, capable of not only perceiving, but also interacting with the environment, allowing the realization of diverse operations and tasks in areas and workspaces that are difficult to access by human operators or ground vehicles [...]

Robotic systems are gradually replacing human intervention in dangerous facilities to improve human safety and prevent risky situations. In this domain, our work addresses the problem of autonomous crossing narrow passages in a semi-structured (i.e., partially-known) environment. In particular, we focus on the CERN’s Super Proton Synchrotron particle accelerator, where a mobile robot platform is equipped with a lightweight arm to perform measurements, inspection, and maintenance operations. The proposed approach leverages an image-based visual servoing strategy that exploits computer vision to detect and track known geometries defining narrow passage gates. The effectiveness of the proposed approach has been demonstrated in a realistic mock-up.

This article proposes a shared-control teleoperation architecture for robot manipulators transporting an object on a tray. Differently from many existing studies about remotely operated robots with firm grasping capabilities, we consider the case in which, in principle, the object can break its contact with the robot end-effector. The proposed shared-control approach automatically regulates the remote robot motion commanded by the user and the end-effector orientation to prevent the object from sliding over the tray. Furthermore, the human operator is provided with haptic cues informing about the discrepancy between the commanded and executed robot motion, which assist the operator throughout the task execution. We carried out trajectory tracking experiments employing an autonomous 7-degree-of-freedom (DoF) manipulator and compared the results obtained using the proposed approach with two different control schemes (i.e., constant tray orientation and no motion adjustment). We also carried out a human-subjects study involving 18 participants in which a 3-DoF haptic device was used to teleoperate the robot linear motion and display haptic cues to the operator. In all experiments, the results clearly show that our control approach outperforms the other solutions in terms of sliding prevention, robustness, commands tracking, and user’s preference.

A hardware-in-the-loop simulator for human cooperation with an aerial manipulator is presented in this paper. The simulator provides the user with realistic haptic feedback proper of a human-aerial manipulator interaction activity. The forces exchanged between the hardware interface and the human/environment are measured and supplied to a dynamically simulated aerial manipulator. In turn, the simulated aerial platform feeds back its position to the hardware allowing the human to feel and evaluate the interaction effects. Besides human-aerial manipulator cooperation, the simulator lends itself to developing and testing autonomous control strategies in aerial manipulation. Therefore, the effectiveness of the proposed system is evaluated along with two case studies: a collaborative task where the human operator attaches a tool to the robot end-effector and an autonomous bird diverter installation task.