This article presents a new hybrid motion (HM) planner, designed to allow robust indoor navigation in constrained environments of nonholonomic differential robots, such as RobChair, the brain-actuated robotic wheelchair from the Institute of Systems and Robotics, University of Coimbra, Portugal. Relying on this new planning algorithm, RobChair is now able to operate in real dynamic environments and perform challenging maneuvers in narrow spaces. The HM planner integrates deliberative and reactive modules in a three-layer structure: a fast three-dimensional (3-D)-global path planner, smoothing, and a new reactive local planner designated the double-dynamic window approach (D-DWA). The 3-D-global path planner consists in the A* algorithm, which defines a path composed by a sequence of (x, y) points, with an interpolation module that has the purpose of assigning an orientation to each (x, y) point. The smoothing algorithm adjusts the (x, y) points to reduce accelerations and jerk associated with the trajectory. The D-DWA is in charge of dynamically adapting the robot motion, taking into account the robot geometry (noncircular robot) and local static/dynamic obstacles, unknown from the global planner perspective. Real-time navigation is achieved because both the smoothing and the D-DWA algorithms are iteratively executed during navigation. The use of multiresolution local grid maps also contributes to the increase of computation performance. To show the effectiveness of the proposed planning algorithm, results of real navigation experiments are reported in this article. The experiments consist in steering RobChair in a real office-like scenario by different participants, using a self-paced P300-based brain-computer interface (BCI).

This paper presents a shared-control approach for Assistive Mobile Robots (AMR), which depends on the user's ability to navigate a semi-autonomous powered wheelchair, using a sparse and discrete human-machine interface (HMI). This system is primarily intended to help users with severe motor disabilities that prevent them to use standard human-machine interfaces. Scanning interfaces and Brain Computer Interfaces (BCI), characterized to provide a small set of commands issued sparsely, are possible HMIs. This shared-control approach is intended to be applied in an Assisted Navigation Training Framework (ANTF) that is used to train users' ability in steering a powered wheelchair in an appropriate manner, given the restrictions imposed by their limited motor capabilities. A shared-controller based on user characterization, is proposed. This controller is able to share the information provided by the local motion planning level with the commands issued sparsely by the user. Simulation results of the proposed shared-control method, are presented.

This paper describes a new modular software architecture designed to allow collaborative Human-Robot Interaction (HRI). The proposed HRI system is applied to the InterBot (Interactive mobile roBot), which integrates two Human-Machine Interface (HMI) devices: an on-board portable device and a remote station. The software architecture is mainly developed using the Robot Operating System (ROS) framework, and is designed to be modular and versatile. The proposed software modules provide a reliable structure for users to interact with and directly control the InterBot, e.g. allowing a human to be guided during a tour, or for a human to order the InterBot to perform tasks such as delivering messages or items between laboratories. The remote station acts as the supervisor, being in charge of planning the robot tasks sequentially, while displaying the robot information in a perceivable way. A series of tests and experiments were carried out in order to assess the performance of the InterBot platform in different test scenarios and under three Human-Robot control modes: teleoperation, autonomous and shared control. Presented real-world tests show the functional flexibility of the proposed architecture and outline future directions of development and research. The results also show that the InterBot software architecture is robust to the occurrence of network delays.

This paper presents an outdoor guidepath navigation system for autonomous mobile robots (AMR) that use permanent magnetic markers embedded in the ground. The odometric data provided by the wheel encoders is fused with the data from magnetic markers. The extended Kalman filter (EKF) was chosen for the fusion process. The AMR is equipped with a magnetic sensing ruler (MSR) developed at ISR-UC that is able to perform a robust detection of magnetic markers. The detection is based on a 3-D algorithm that includes longitudinal-fitting detection (LFD), and cross-fitting detection (CFD). Both, the LFD and the CFD are based on the least squares fitting (LSF) of the measurement data with the 3-D model of the vertical magnetic field. The experimental results with Robchair (intelligent wheelchair being developed at ISR-UC) primarily show that the detection system is robust, since it is able to detect true magnetic markers, and to eliminate noisy magnetic distortions and false markers. The design, and implementation of the navigation algorithm in the Robchair were carried out, and results are presented.

Remote monitoring of health parameters is a promising approach to improve the health condition and quality of life of particular groups of the population, which can also alleviate the current expenditure and demands of healthcare systems. The elderly, usually affected by chronic comorbidities, are a specific group of the population that can strongly benefit from telehealth technologies, allowing them to reach a more independent life, by living longer in their own homes. Usability of telehealth technologies and their acceptance by end-users are essential requirements for the success of telehealth implementation. Older people are resistant to new technologies or have difficulty in using them due to vision, hearing, sensory and cognition impairments. In this paper, we describe the implementation of an IoT-based telehealth solution designed specifically to address the elderly needs. The end-user interacts with a TV-set to record biometric parameters, and to receive warning and recommendations related to health and environmental sensor recordings. The familiarization of older people with the TV is expected to provide a more user-friendly interaction ensuring the effectiveness integration of the end-user in the overall telehealth solution.