Autonomy, cooperation and data fusion can increase the performance of robotic networks in many underwater applications. In this paper, we describe a novel occupancy grid (OG) based perception layer, and its use for controlling a network of autonomous underwater vehicles (AUVs), sensorised with passive sonars. Data fusion between the robots' bearing-only measurements (typical of passive sonars) enables the estimate of target position. The developed OG framework exploits networking and the spatial diversity provided by the multi-robot system. The perception layer was integrated in the intelligent Cooperative Autonomous Decision Making Engine (iCADME) control architecture and validated for the first time in the Robotics Experimentation and Prototyping MUS (REPMUS) Exercise, held in Portugal in September 2021. Our robotic network participated in a technical demonstration, whose main objective was to localise a bottomed submarine which emitted a periodic help request acoustically during a simulated distress situation. We report results which are one of the first examples to demonstrate how cooperative robotics, supported by data fusion, can be effective in a passive sonar scenario. They also confirm the viability of adopting such solutions in real-world applications, characterised by poor communications and challenging environments. What was achieved at REPMUS21 clearly demonstrates how a network of cooperative robots can improve search & rescue operations of a submarine.

Cooperation among multiple autonomous surface and underwater vehicles is an important capability for detection and tracking of underwater objects. Cooperative autonomy in the underwater environment, however, is challenged by the communication bandwidth. In this work, we propose a selective communication scheme that underpins collaborative surveillance under communication constraints. This scheme classifies signal reflections of sonar pulses that are detected by on-board sensor processing as contacts with the object of interest or background using a convolutional neural network. This network is trained using previously labelled contact spectrograms obtained during three sea trials carried out between 2016-2018. The classification scores at the CNN output are ordered to select the few contacts that the underwater modem bandwidth allows for transmission to the network. First, we evaluate the accuracy of the data-driven information selection scheme using recall scores and similar performance measures. Then, we find the accuracy in Bayesian recursive filtering (tracking) of these contacts for different communication rates using established error metrics. The results suggest that the selective scheme yields a favourable surveillance performance communication cost trade-off.

The necessity of long-range underwater surveillance has strongly increased in the last decades, and low-frequency active sonar (LFAS) systems seem to fulfill this need. However, in littoral environments with shallow water LFAS may suffer from an elevate number of false alarms. In this context, the capability to distinguish between object-generated and clutter-generated detections is crucial. This paper describes a multiobject tracking framework based on the sum-product algorithm that exploits the information provided by a convolutional neural network that classifies the LFAS detections. The effectiveness of the proposed approach is demonstrated both in a simulated and in a real underwater scenario.

Cooperative autonomy and data sharing can largely improve the mission performance of robotic networks in underwater surveillance applications. In this paper, we describe the cooperative autonomy used to control the Autonomous Underwater Vehicles (AUVs) acting as sonar receiver nodes in the CMRE Anti-Submarine Warfare (ASW) network. The paper focuses on a track management module that was integrated in the robot autonomy software for enabling the share of information. Track to track (T2T) associations are used for improving track classification and for creating a common tactical picture, necessary for AUV cooperative strategies. We also present a new cooperative data-driven AUV behaviour that exploits the spatial diversity of multiple robots for improving target tracking and for facilitating T2T associations. We report results with real data collected at sea that validate the approach. The reported results are one of the first examples that show the potential of cooperative autonomy and data fusion in realistic underwater surveillance scenarios characterised by limited communications.

This work describes how the localisation of autonomous underwater vehicles can be supported through networked acoustic communication. The localisation approach includes timing information within acoustic messages to create an interrogation scheme similar to that of long-baseline methods, but realised at the application level of the network. In this way, the network itself is able to provide vehicle localisation information, reducing the needs for additional on-board sensors or dedicated deployed platforms/transponders. The aim of this work is to report at sea localisation results as obtained in two completely different application scenarios. The first one is represented by the FP7 MORPH project (HORTA15 sea trial) where the proposed approach has been applied to support navigation of a fleet of AUVs in a tight formation and in very shallow waters. The second application is represented by the NATO STO CMRE multistatic network demonstrator with the acoustic network used to support navigation of AUVs working in large operational areas. In this latter case, results are given from the COLLAB-NGAS14 experimental campaign.

The solution of the inverse kinematics problem of soft manipulators is essential to generate paths in the task space. The inverse kinematics problem of constant curvature or piecewise constant curvature manipulators has already been solved by using different methods, which include closed-form analytical approaches and iterative methods based on the Jacobian method. On the other hand, the inverse kinematics problem of nonconstant curvature manipulators remains unsolved. This study represents one of the first attempts in this direction. It presents both a model-based method and a supervised learning method to solve the inverse statics of nonconstant curvature soft manipulators. In particular, a Jacobian-based method and a feedforward neural network are chosen and tested experimentally. A comparative analysis has been conducted in terms of accuracy and computational time.

This paper addresses the problem of developing an autonomous cooperative multi-vehicle system composed of one simple low-cost unmanned underwater vehicle (UUV) guided by a more capable, sensor-equipped, surface or underwater vehicle. Specifically, in this work we consider a system composed of an autonomous surface vehicle (ASV) able to localize a small-sized UUV via a multibeam sonar and, then, to guide it via an acoustic link to reach a target. The UUV is equipped with low-cost navigation sensors (a compass and a depth sensor), and to estimate its own position relies on the data received by the ASV (range and bearing from the ASV and ASV position). This paradigm was applied in the domain of mine countermeasures (MCM) to realize the NATO CMRE Autonomous Mine Neutralization System. In this system the low-cost feature of the UUV is of the most importance since the UUV is supposed to be expendable. In this approach, however, there may be cases in which the underwater vehicle cannot be detected by the sonar for extended time periods causing drifts in the UUV position estimate, potentially compromising the mission. This paper presents a set of behaviors to address this situation. The behaviors coordinate ASV circular search patterns together with sending to the UUV purposely generated navigation updates. The aim is to limit the distance between the two vehicles, increasing the probability of sonar reacquisition, and at the same time ensuring that the UUV progresses toward the target. Results from sea trials held in Elba island (Italy) during ANT12 experiments (in June 2012) are reported, and demonstrate the approach is effective and can push our system toward full autonomy.

In this work we address the inverse kinetics problem of a non-constant curvature manipulator driven by three cables. An exact geometrical model of this manipulator has been employed. The differential equations of the mechanical model are non-linear, therefore the analytical solutions are difficult to calculate. Since the exact solutions of the mechanical model are not available, the elements of the Jacobian matrix can not be calculated. To overcome intrinsic problems of the methods based on the Jacobian matrix, we propose for the first time a neural network learning the inverse kinetics of the soft manipulator moving in three-dimensional space. After the training, a feed-forward neural network (FNN) is able to represent the relation between the manipulator tip position and the forces applied to the cables. The results show that a desired tip position can be achieved with a degree of accuracy of 1.36% relative average error with respect to the total arm length.

Control of soft robots remains nowadays a big challenge, as it does in the larger category of continuum robots. In this paper a direct and inverse kinetics models are described for a non-constant curvature structure. A major effort has been put recently in modelling and controlling constant curvature structures, such as cylindrical shaped manipulators. Manipulators with non-constant curvature, on the other hand, have been treated with a piecewise constant curvature approximation. In this work a non-constant curvature manipulator with a conical shape is built, taking inspiration from the anatomy of the octopus arm. The choice of a conical shape manipulator made of soft material is justified by its enhanced capability in grasping objects of different sizes. A different approach from the piecewise constant curvature approximation is employed for direct and inverse kinematics model. A continuum geometrically exact approach for direct kinetics model and a Jacobian method for inverse case are proposed. They are validated experimentally with a prototype soft robot arm moving in water. Results show a desired tip position in the task-space can be achieved automatically with a satisfactory degree of accuracy.

-We describe the design, realization and first sea trials of a small-sized Autonomous Surface Vehicle (ASV) for environmental monitoring. The robot is being developed in the framework of the HydroNet European project [1] aiming at realizing a new multi-robot system for monitoring the quality of coastal waters, rivers and lagoons. One main innovation of the robot is the capability to measure heavy metals concentrations directly onboard using sensors ad hoc developed by the consortium. This enables the system to provide real-time measurements of heavy metals concentrations potentially changing the current water monitoring methodology in which the samples are collected by a dedicated boat and analyzed in laboratory. The robot is designed for long range missions and for lodging the onboard water analysis system. Some severe constraints imposed by the addressed scenarios are considered in the design: reduced length and limited weight for ease of transportability and deployment; low draft and protected propellers to enable the ASV to move safely in shallow waters with likely floating obstacles such as plastic bags.

We report on the design and the experimental results of DustCart, a wheeled autonomous robot for door-to-door garbage collection. DustCart is able to navigate in urban environments avoiding static and dynamic obstacles and to interact with human users. The robot is managed by an Ambient Intelligence system (AmI) through a wireless connection: it navigates to collect garbage bags to the houses of users and then moves to discharge the collected waste to a dedicated area. The architecture, navigation and localization systems are described along with the results achieved in different urban sites. In particular, a localization approach based on optical beacons was used and guaranteed position errors sufficient for a safe robot navigation. We report also the first results of a long-term experimentation of the DustCart robot in Peccioli, a small town of Tuscany (Italy). This can be considered as a first step in using robotics in the everyday life of a real town for providing a real service.

Water, recognized as one of the most important and endangered resources to mankind, is very difficult to monitor in real time using conventional methods. Thanks to recent advancements in technology, the use of robots, able to fulfill missions such as sampling from aquatic environments, becomes feasible and can increase dramatically the quality of water monitoring. Due to the complications for the robot's movement in some aquatic scenarios, for instance, shallow waters or dry banks of rivers and lakes, amphibian locomotion appears to be necessary to guarantee a satisfying coverage of the monitoring activities in such areas. In this paper, we focus on developing a practical amphibian solution by introducing a pair of screw rotors, which rotate in opposite directions to generate locomotion on the ground. The primitive idea of this novel design is to enhance the versatility of a marine robot with a terrestrial locomotion without undermining its performance during the movements in the water. In particular, we elaborate the principle of this novel design and present some test results of a prototype on selected terrain types, which are similar to the target aquatic environment. We conclude the paper with some preliminary conclusions based on the analysis of the test results about effectiveness and efficiency issues.

This paper describes and discusses the preliminary results of a behavioural study on robot social acceptability, which was carried out during a public demonstration in South Korea. Data was collected by means of direct observation of people behaviour during interaction with robots. The most interesting result to emerge is that of young people: they tended to react to the robots presence with extreme curiosity and, quite often, to treat them aggressively. In this paper, the word bullying is used to describe any kind of improper and violent behaviour, intended to cause damages or impede the robot operation. It is the authors' opinion that if not tackled appropriately, abuses towards robots may become a serious hindrance to their future deployment, and safety. Hence, the necessity to tackle this issue with dedicated solutions during the early phases of design.

In this paper we address the problem of localizing active hydrothermal vents on the seafloor using an autonomous underwater vehicle (AUV). Woods Hole Oceanographic Institution's Autonomous Benthic Explorer (ABE) (see Fig.1) AUV has been successfully used in several hydrothermal vents prospecting missions. Recently, a three-stage nested approach [1] has been introduced. It consists in surveys at different altitudes to finally photograph the venting structures flying very close to the seafloor. In this paper, we introduce a new strategy for ABE's movement in surveys at higher altitude (phase-1): ABE moves along predesigned tracklines sampling the seawater; when some conditions on collected data are encountered, it starts a spiral movement to fly over areas likely to contain active vent fields. Results of the proposed algorithm tested on data coming from previous ABE's missions are shown and assess the efficiency and reliability of the method.

This paper addresses the problem of autonomous localization of multiple gas or odor sources in an indoor environment with no strong airflow. In our approach, a robot iteratively builds an occupancy grid map from successive measurements of odor concentration. The resulting map shows the probability of each discrete cell in the map containing an active plume source. Our method is based on a recent adaptation of Bayesian occupancy grid mapping (OGM) to the chemical plume source localization problem. We present experimental results that demonstrate the utility of the approach.