Human environments are often unstructured and unpredictable, thus making the autonomous operation of robots in such environments is very difficult. Despite many remaining challenges in perception, learning, and manipulation, more and more studies involving assistive robots have been carried out in recent years. In hospital environments, and in particular in patient rooms, there are well-established practices with respect to the type of furniture, patient services, and schedule of interventions. As a result, adding a robot into semi-structured hospital environments is an easier problem to tackle, with results that could have positive benefits to the quality of patient care and the help that robots can offer to nursing staff. When working in a healthcare facility, robots need to interact with patients and nurses through Human-Machine Interfaces (HMIs) that are intuitive to use, they should maintain awareness of surroundings, and offer safety guarantees for humans. While fully autonomous operation for robots is not yet technically feasible, direct teleoperation control of the robot would also be extremely cumbersome, as it requires expert user skills, and levels of concentration not available to many patients.
Therefore, in our current study we present a traded control scheme, in which the robot and human both perform expert tasks. The human-robot communication and control scheme is realized through a mobile tablet app that can be customized for robot sitters in hospital environments. The role of the mobile app is to augment the verbal commands given to a robot through natural speech, camera and other native interfaces, while providing failure mode recovery options for users. Our app can access video feed and sensor data from robots, assist the user with decision making during pick and place operations, monitor the user health over time, and provides conversational dialogue during sitting sessions. In this paper, we present the software and hardware framework that enable a patient sitter HMI, and we include experimental results with a small number of users that demonstrate that the concept is sound and scalable.
The performance of robots to carry out tasks depends in part on the sensor information they can utilize. Usually, robots are fitted with angle joint encoders that are used to estimate the position and orientation (or the pose) of its end-effector. However, there are numerous situations, such as in legged locomotion, mobile manipulation, or prosthetics, where such joint sensors may not be present at every, or any joint. In this paper we study the use of inertial sensors, in particular accelerometers, placed on the robot that can be used to estimate the robot pose. Studying accelerometer placement on a robot involves many parameters that affect the performance of the intended positioning task. Parameters such as the number of accelerometers, their size, geometric placement and Signal-to-Noise Ratio (SNR) are included in our study of their effects for robot pose estimation. Due to the ubiquitous availability of inexpensive accelerometers, we investigated pose estimation gains resulting from using increasingly large numbers of sensors. Monte-Carlo simulations are performed with a two-link robot arm to obtain the expected value of an estimation error metric for different accelerometer configurations, which are then compared for optimization. Results show that, with a fixed SNR model, the pose estimation error decreases with increasing number of accelerometers, whereas for a SNR model that scales inversely to the accelerometer footprint, the pose estimation error increases with the number of accelerometers. It is also shown that the optimal placement of the accelerometers depends on the method used for pose estimation. The findings suggest that an integration-based method favors placement of accelerometers at the extremities of the robot links, whereas a kinematic-constraints-based method favors a more uniformly distributed placement along the robot links.
Poly(3,4-ethyle- nedioxythiophene)-poly(styrenesulfonate) or PEDOT:PSS is a flexible polymer which exhibits piezo-resistive properties when subjected to structural deformation. PEDOT:PSS has a high conductivity and thermal stability which makes it an ideal candidate for use as a pressure sensor. Applications of this technology includes whole body robot skin that can increase the safety and physical collaboration of robots in close proximity to humans. In this paper, we present a finite element model of strain gauge touch sensors which have been 3D-printed onto Kapton and silicone substrates using Electro-Hydro-Dynamic ink-jetting. Simulations of the piezoresistive and structural model for the entire packaged sensor was carried out using COMSOLR , and compared with experimental results for validation. The model will be useful in designing future robot skin with predictable performances.
As the use of robots increases for tasks that require human-robot interactions, it is vital that robots exhibit and understand human-like cues for effective communication. In this paper, we describe the implementation of object tracking capability on Philip K. Dick (PKD) android and a gaze tracking algorithm, both of which further robot capabilities with regard to human communication. PKD’s ability to track objects with human-like head postures is achieved with visual feedback from a Kinect system and an eye camera. The goal of object tracking with human-like gestures is twofold: to facilitate better human-robot interactions and to enable PKD as a human gaze emulator for future studies. The gaze tracking system employs a mobile eye tracking system (ETG; SensoMotoric Instruments) and a motion capture system (Cortex; Motion Analysis Corp.) for tracking the head orientations. Objects to be tracked are displayed by a virtual reality system, the Computer Assisted Rehabilitation Environment (CAREN; MotekForce Link). The gaze tracking algorithm converts eye tracking data and head orientations to gaze information facilitating two objectives: to evaluate the performance of the object tracking system for PKD and to use the gaze information to predict the intentions of the user, enabling the robot to understand physical cues by humans.
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