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A Fault Tolerant Control (FTC) scheme for robotic manipulators affected by actuator faults is proposed in this paper. The actuator fault consists in an unknown partial joint torque reduction, which causes a loss of the desired end-effector motion. The FTC scheme includes a Fault Detection and Diagnosis (FDD) system based on a first-order sliding mode observer, in order to detect and estimate the joint torque fault. The estimated fault is mapped into a kinematic deviation of the motion of the end effector from the desired one, and compensated at the level of the kinematic controller. Simulation results demonstrate the effectiveness of the proposed scheme for a planar manipulator affected by two types of actuator faults, namely bias fault and partial torque fault.

The joint position constraints of two cooperative robotic manipulators is a critical issue, especially when both manipulators grasp and move the same object. When two robots cooperate to move a grasped object from one point to another one, following a desired path, the general solution adopted in the literature is to use the kinematic redundant motions possessed by a single manipulator, in order to avoid joint constraints. However, this method requires a number of redundant motions at least equals to the number of joint constraints to satisfy. The study proposed in this paper is focused on developing a joint position limits avoidance strategy, which is able to satisfy all joint limits even when the number of redundant motions are no longer sufficient to ensure them with a classical approach. This is achieved by means of a supervisory control system, which allows to locally and temporary change the desired end-effectors motion, when the redundant motions are not available. The supervisory control sacrifices the path following task in order to ensure joint position limits avoidance, while preserving at the same time the relative end-effector motion, such that the stability of the grasped object is still ensured. The proposed strategy has been implemented on a commercial robot, namely Baxter, which possesses two anthropomorphic arms having one degree of redundancy. The obtained results prove the effectiveness of the proposed supervisor controller, which can be easily extended and applied in manufacturing scenario. Whereas path following can be temporary sacrificed (e.g., the grasped object can be moved from one point to another in different ways), the joint constraints must be strictly preserved for safety reasons.

In the last years, the development of robots for assisting and collaborating with people has experienced a large growth. Applications for assistive robots include hospital service robots, factory intelligent assistants and personal homecare robots. Working in shared environments with human beings, these robots require effective ways to achieve an increasing human-robot cooperation. This work presents a possible approach for performing human-robot cooperation, namely recognition of a user selected object by means of Brain Computer Interface (BCI), followed by pick and place via a robotic arm. The object selection is achieved introducing a BCI that allows the user, after a training phase, to choose one among six different objects of common diffusion. The selection is achieved by interpreting the P300 signals generated in the brain, when the image of the object, desidered by the user, appears on a computer screen as a visual stimulus. The robot then recognizes, through a classifier, the selected object among others within its workspace, and inscribes it in a rectangle shape. Finally, the robot arm is moved in correspondence to the object position, the gripper is rotated according to the object orientation and the object grasped and moved into a different position on a desk in front of the robot. This system could support people with limited motor skills or paralysis, playing an important role in structured assistive environments in a near future.

The present study aims at providing a robotic architecture system composed by a semi-autonomous mobile robot and a manipulator robot. The user, on a smart wheelchair, is able to move in an indoor environment, by selecting, through a user-interface, the room where he/she wants to move and the object he/she would like to get. In case an object is selected, the user is automatically driven in front of a robotic workstation, where the desired object is picked and placed by a robotic arm in front of the user. The system has been tested on a simulator, and preliminary experimental results show the feasibility of the proposed architecture.

A kinematic Fault Tolerant Control (FTC) scheme for a dual-arm system affected by actuator faults is proposed in this paper. The actuator fault consists in an unknown partial joint torque reduction, which causes a loss of the desired end-effector motion. The kinematic controller is designed according to the Relative Jacobian method, while a hierarchic prioritized tasks architecture is exploited in order to perform secondary tasks. The FTC scheme includes a Fault Detection and Diagnosis (FDD) system based on first-order sliding mode observers, in order to detect and estimate the joint torque faults on the system. The estimated fault is mapped into a perturbation of the motion of the end effector from the desired one, and compensated at the kinematic controller level. Simulation results demonstrate that the proposed technique allows to reduce the tracking error (both for the absolute and the relative motion) generated by the fault for a dual-arm system composed of two planar manipulators.

This paper proposes an assistive robotic solution for elderly people and/or people suffering from lower limbs problem. The proposed architecture consists of a Smart Wheelchair (SWC) and a Robotic Workstation (RW) which cooperate in order to perform an autonomous navigation and manipulation task. The system allows the user to choose a point on the map to move to and/or an object to manipulate. The SWC is able to autonomously navigate to the desired RW, while the localization is performed via an Unscented Kalman Filter (UKF). Moreover, QR codes have been used as landmarks to compensate the pose estimation error. The system was tested both in simulation and in a real scenario.

Ahstract─ This work proposes a robotic manipulator assistant for disabled users and/or for elderly people with limited motor skills. In detail, the interaction among the robot and the user is based on the user gestures recognition. The user chooses an object among those available by moving his/her arm in a specific pose, which is recognized by using an external camera. Then, images of the objects accessible to the robot are acquired via the robot camera, located at the end of the robot arm, and are analyzed by a Support Vector Machine classifier in order to recognize the user selected object. Finally, the manipulator picks the object and places it on the user's hand, whose location in the Cartesian space is determined via the external camera and updated online.

In this paper a robust Fall Detection Algorithm by using a deep learning approach and a low-cost mobile robot equipped with an RGB camera is presented. This method consists of four steps. The first step is the user detection, achieved by a real-time video stream and a Deep Learning approach. Once the user is detected, then its position is estimated in the second step. In the third step, if a fall is detected, a photo is acquired and a pre-registered audio message asks the user how he is. In the last step the photo and the audio captured are sent to a Telegram Bot (TB) in order to alert family members or caregivers. Tests have been performed in a real scenario.

This paper proposes a system for recognizing human complex activities by using unobtrusive sensors such as smartphone, smartwatch and bluetooth beacons. The method encapsulates two classification stages. The former is composed of two parallel processes: the Main Activity Detection (MAD) and the Room Detection (RD). The latter implements the Complex Activity Detection (CAD) process by exploiting the outputs of the first stage and the accelerometer data of the smartwatch. The cascade classification approach that combines the room detection with the main/complex activities recognition task constitutes the novelty of the work. Preliminary results demonstrate the reliability of the system in terms of accuracy and macro-Fl score.

In an energy management (EM) perspective, an objective function considering the balance of energy and resources needs to be optimized. To reach the goal, in a residential scenario, a number of tasks (typically appliances) can be managed (e.g. shifted). This paper proposes an unobtrusive cascade classifier approach to autonomously identify the different appliances and their priorities (shiftable, non interruptible, continuous loads) in EM algorithms based on smart plugs measures.

An unobtrusive method to realize human fall detection by using bluetooth beacons, a smartphone and a low cost mobile robot is presented. The method is composed by five steps. The first consists in extracting features from the smartphone acceleration data, which are then analysed online by the fall detection algorithm. Once the fall event is detected, then the location is determined by using the bluetooth signal received from beacons. Then, the mobile robot moves towards the user's location, and finally verifies if the detected fall event is a true positive or not, through a procedure based on voice interaction with the potentially fallen user. The method has been tested in laboratory, proving to be a viable solution to perform fall detection in smart homes via consumer devices.

In the new European scenario the design of a photovoltaic plant ensuring savings on electricity bills is strongly related to energy management policies. This paper introduces a low-cost device to monitor domestic electricity consumptions, photovoltaic production and define load shifting policies based on fuzzy logic forecasts.