The paper presents a shared control architecture for non-holonomic mobile robots commanded through a motor imagery based Brain-Computer Interface (BCI). The overall system is aimed at assisting people to teleoperate a mobile robot in a simulated house-like scenario by resorting to two motor imagery commands. The developed architecture is structured in such a way that the user can drive the mobile robot while safety tasks, e.g. obstacle avoidance, are autonomously achieved, leaving complete autonomy to the mobile robot to let the latter adjust its configuration, e.g. aligning itself with a narrow passage. The overall architecture has been realized by developing control modules with the ROS environment, while the OpenVibe framework has been adopted to process the EEG signals. The effectiveness of the proposed architecture has been validated through experiments where a healthy user, wearing a Unicorn g.tec BCI, performs an assisted teleoperation task through motor imagery sessions with a Turtlebot robot.

This paper considers a scenario where a robot and a human operator share the same workspace, and the robot is able to both carry out autonomous tasks and physically interact with the human in order to achieve common goals. In this context, both intentional and accidental contacts between human and robot might occur due to the complexity of tasks and environment, to the uncertainty of human behavior, and to the typical lack of awareness of each other actions. Here, a two stage strategy based on Recurrent Neural Networks (RNNs) is designed to detect intentional and accidental contacts: the occurrence of a contact with the human is detected at the first stage, while the classification between intentional and accidental is performed at the second stage. An admittance control strategy or an evasive action is then performed by the robot, respectively. The approach also works in the case the robot simultaneously interacts with the human and the environment, where the interaction wrench of the latter is modeled via Gaussian Mixture Models (GMMs). Control Barrier Functions (CBFs) are included, at the control level, to guarantee the satisfaction of robot and task constraints while performing the proper interaction strategy. The approach has been validated on a real setup composed of a Kinova Jaco2 robot.

The paper presents a shared control architecture for robotic systems commanded through a motor imagery based Brain-Computer Interface (BCI). The overall system is aimed at assisting people to perform teleoperated manipulation tasks, and it is structured so as to leave different levels of autonomy to the user depending on the actual stage of the task execution. The low-level part of the shared control architecture is also in charge of taking into account safety and operational tasks, such as to avoid collisions or to manage robot joint limits. The overall architecture has been realized by integrating control and perception software modules developed within the ROS environment, with the OpenVibe framework used to operate the BCI device. The effectiveness of the proposed architecture has been validated through experiments where a healthy user, wearing a Unicorn g.tec BCI, performs an assisted task through motor imagery sessions, with a 7 Degrees of Freedoms Kinova Jaco2 robotic arm.

The paper presents a Distributed Fault Detection and Isolation strategy for a team of mobile manipulators performing a cooperative mission. The overall system relies on an observer-controller scheme where each robot estimates the global state of the team through a distributed observer; then, the global state estimate is used by each robot to compute the estimated local input so as to achieve a specific global task. The observer-controller scheme also allows to define a set of residual vectors that can be used by the robots to detect and isolate faults affecting any member of the team, even if not in direct communication, and without increasing the computational burden and the information exchange. The approach is validated via numerical simulations with a team of four mobile manipulators performing a transportation mission.

In this paper we present a framework that allows the motion control of a robotic arm automatically handling different kinds of safety-related tasks. The developed controller is based on a Task-Priority Inverse Kinematics algorithm that allows the manipulator's motion while respecting constraints defined either in the joint or in the operational space in the form of equality-based or set-based tasks. This gives the possibility to define, among the others, tasks as joint-limits, obstacle avoidance or limiting the workspace in the operational space. Additionally, an algorithm for the real-time computation of the minimum distance between the manipulator and other objects in the environment using depth measurements has been implemented, effectively allowing obstacle avoidance tasks. Experiments with a Jaco2manipulator, operating in an environment where an RGB-D sensor is used for the obstacles detection, show the effectiveness of the developed system.

In this paper we present an architecture for the operation of an assistive robot finally aimed at allowing users with severe motion disabilities to perform manipulation tasks that may help in daily-life operations. The robotic system, based on a lightweight robot manipulator, receives high level commands from the user through a Brain-Computer Interface based on P300 paradigm. The motion of the manipulator is controlled relying on a closed loop inverse kinematic algorithm that simultaneously manages multiple set-based and equality-based tasks. The software architecture is developed relying on widely used frameworks to operate BCIs and robots (namely, BCI2000 for the operation of the BCI and ROS for the control of the manipulator) integrating control, perception and communication modules developed for the application at hand. Preliminary experiments have been conducted to show the potentialities of the developed architecture.

The paper deals with the problem of decentralized fault detection, isolation and recovery for teams of networked robots. The proposed strategy is a combination of distributed and local approaches that allow the robots to deal with both recoverable and unrecoverable faults. A local adaptive fault observer is used to locally compensate recoverable faults, while a distributed fault detection and isolation strategy is used to allow each robot to detect unrecoverable faults on other teammates even if not directly connected; once the faulty robots have been isolated, they are removed from the team and the mission is rearranged. Results of numerical simulations and experiments involving a team of 5 mobile robots are provided to show the effectiveness of the approach.

In this paper, a decentralized control strategy for networked multi-robot systems that allows the tracking of the team centroid and the relative formation is presented. The proposed solution consists of a distributed observer-controller scheme where, based only on local information, each robot estimates the collective state and tracks the two assigned control variables. We provide a formal stability analysis of the observer-controller scheme and we relate convergence properties to the topology of the connectivity graph. Experiments are presented to validate the approach.

The paper characterizes the localization performance of an Autonomous Underwater Vehicle (AUV) when it moves in environments where floating drifters or surface vessels are present and can be used for relative localization. In particular, we study how localization performance is affected by parameters e.g. AUV mobility, surface objects density, the available measurements (ranging and/or bearing) and their visibility range. We refer to known techniques for estimation performance evaluation and probabilistic mobility models, and we bring them together to provide a solid numerical analysis for the considered problem. We perform an extensive simulations in different scenarios, and, as a proof of concept, we show how an AUV, equipped with an upward looking sonar, can improve its localization estimate by detecting a surface vessel.

In this paper a decentralized controller-observer scheme for centroid tracking with a multi-robot system is presented. The key idea is to develop, for each robot, an observer of the collective system's state; each local observer is updated by only using information of the state of the robot and of its neighbors. The local observers' estimations are then used by the individual robots to cooperatively track an assigned time-varying reference for the weighted centroid. Convergence of the scheme is proven for both fixed and switching communication topologies, as well as for directed and undirected communication graphs. Numerical simulations relative to different case studies are illustrated to validate the approach.

This paper addresses the problem of observability of the relative motion of two AUVs equipped with velocity and depth sensors, and inter-vehicle ranging devices. We start by exploiting nonlinear observability concepts to analyze, using observability rank conditions, some types of relative AUV motions. Because rank conditions only provide binary information regarding observability, we then derive a specific observability index (metric) and study its dependence on the types of relative motions executed by the vehicles. In particular, it is shown that the degradation of observability depends on the range and angle between the relative velocity and position vectors. The problem addressed bears affinity with that of single beacon localization. For this reason, the results derived are validated experimentally in a equivalent, single beacon navigation scenario.

The paper studies the observability properties of the relative localization of two Autonomous Underwater Vehicles (AUVs) equipped with depth sensors, linear/angular velocity sensors, and communication devices with range measurement. The conditions that ensure observability of the linearized model and locally weak observability of the nonlinear system are derived. An Extended Kalman Filter is then designed aimed at estimating the relative position between two AUVs. Simulations in 3D and reconstruction from experimental data in 2D provide a numerical validation of the analysis.

We present a study on the use of cooperative robots to execute a caging mission on the water's surface. In particular, we consider the problem of using two robotic boats (under-actuated autonomous surface vessels) connected with a floating rope, to `capture' a floating object from a known location on the water's surface and 'shepherd' it to a designated position. This paper focuses on the cooperative control strategy of the two vessels. Each vessel's behavior is governed by a supervisor software module that handles the communication with the other vessel and controls all elementary tasks that compose the overall mission. The elementary tasks, specifically developed for under-actuated vessels, are arranged by priority, and merged using a behavior-based approach, namely the Null-Space based Behavioral control. The proposed technique is validated by field experiments with two autonomous robotic boats on the surface of a lake.

This paper presents the application of the Null-Space based Behavioral (NSB) approach to the motion control of a non-holonomic mobile robot with velocity saturated actuators. In particular, the proposed solution aims at managing actuator velocity saturations by dynamically scaling task velocity commands so that the hierarchy of task priorities is preserved in spite of actuator velocity saturations. The approach is tested on a specific case study where the NSB approach elaborates the motion directives for a mobile robot that has to reach a target while avoiding a punctual obstacle. The approach is validated by numerical simulations and by experimental results with a non-holonomic mobile robot.

This paper presents the application of the null-space based behavioral (NSB) approach to the motion control of a team of mobile robots with velocity saturated actuators. In particular, the proposed solution aims at managing actuator velocity saturations by dynamically scaling task velocity commands so that the hierarchy of task priorities is preserved in spite of actuator velocity saturations. The approach is tested on a specific case study where the NSB elaborates the motion directives for a team of six mobile robots that has to entrap and to escort a target. The approach is validated by numerical simulations and by experimental results.

In this paper the flocking problem for a multi-robot system, consisting in making the robots of a team grouping together, is addressed. The flocking is achieved resorting to the Null-Space-based Behavioral (NSB) control by defining very simple behaviors for each robot of the team and by properly arranging these behaviors in priority. The NSB algorithm, making the robots using only local information, successfully achieves the flocking with or without a rendez-vous point and in eventual presence of obstacles. Extensive simulations and experiments using differential-drive mobile robots prove the effectiveness of the proposed algorithm.

In this paper, two experimental case studies performed with a multi-robot system made of 6 Khepera II mobile robots are presented. The experiments, performed at the laboratory LAI (Laboratorio di Automazione Industriale) of the Universita degli Studi di Cassino, are aimed at testing the performances and the robustness of a behavior-based technique, namely the null-space-based behavioral control (NSB), while executing different kinds of missions. In particular, the NSB approach, based on an inverse kinematic technique inherited by industrial manipulator applications, has been developed to control a generic team of autonomous vehicles and it has been implemented on a centralized architecture to control, at a kinematic level, a platoon of autonomous mobile robots.

In this paper the application of a behavior-based control approach, namely the null-space-based behavioral control, to coordinate a fleet of autonomous surface vessels is presented. The NSB can be considered as a centralized guidance system aimed at driving the fleet in complex environments while simultaneously performing multiple tasks, i.e., obstacle avoidance or keeping a formation. In order to apply the guidance system to a fleet of underactuated surface vessels, the NSB works in combination with a low-level maneuvering control that, taking care of the dynamics of the vessels, elaborates the motion commands to generate the generalized forces at the actuators. The guidance system has been simulated in the accomplishment of a mission in presence of obstacles and sea current in the environment

This paper investigates the implementation of a wireless mobile ad-hoc network to guarantee that an autonomously driven mobile vehicle remains connected to a limited-coverage base antenna during its motion. To the purpose, the use of a platoon of mobile robots is proposed to carry a number of repeater antennas; these must be suitably moved to dynamically ensure a multi-hop communication link to the vehicle that extends outside the area covered by the sole base antenna. Self configuration of the robots' platoon is then achieved by a singularity-robust task-priority inverse kinematics algorithm via the definition of suitable task functions. The obtained simulation results show the effectiveness of the proposed approach.