Daniele Proietti Pagnotta

Smart wheelchairs are in the category of assistive robots, which interact physically and/or non-physically with people with physical disabilities to extend their autonomy. Smart wheelchairs are assistive robots that enhance mobility, and can be especially useful for improving access to university premises. This paper proposes a smart wheelchair that can be integrated with an academic management system to enable students who have serious leg problems and cannot walk on their own to reach any academic building or room on a university campus autonomously. The proposed smart wheelchair receives information from the academic management system about the spaces on campus, the lesson schedule, the office hours of lecturers, and so on. Students can select the desired task from the user interface. The smart wheelchair can then guide the student autonomously to the desired point of interest, while planning the best barrier-free route inside the campus/building and, simultaneously, avoiding fixed and moving obstacles. The assistive robot has localization and navigation capabilities, which allow students to move about campus freely and autonomously, and benefit from a barrier-free education.

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.

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 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.

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 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.

Robotic assistance for disabled users and/or for elderly people represents an active research area and, at the same time, a growing market, which will lead to commercial technological solutions in a near future. Cooperation of existing assistive technologies can represent an important step towards the development of such solutions. In this paper, we face the problem of cooperation between a smart wheelchair and one or more robotic workstations: the wheelchair can navigate autonomously and permits to choose the point to move to or the object to interact with, while the robotic workstation can interact with one or more objects within its workspace and with the user on the wheelchair. The hardware/software architecture is here presented, together with some preliminary results.