Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks

Social intelligence is an important requirement for enabling robots to collaborate with people. In particular, human path prediction is an essential capability for robots in that it prevents potential collision with a human and allows the robot to safely make larger movements. In this paper, we present a method for predicting the trajectory of a human who follows a haptic robotic guide without using sight, which is valuable for assistive robots that aid the visually impaired. We apply a deep learning method based on recurrent neural networks using multimodal data: (1) human trajectory, (2) movement of the robotic guide, (3) haptic input data measured from the physical interaction between the human and the robot, (4) human depth data. We collected actual human trajectory and multimodal response data through indoor experiments. Our model outperformed the baseline result while using only the robot data with the observed human trajectory, and it shows even better results when using additional haptic and depth data.Comment: 6 pages, Submitted to IEEE World Haptics Conference 201

Vision-based interface applied to assistive robots

This paper presents two vision-based interfaces for disabled people to command a mobile robot for personal assistance. The developed interfaces can be subdivided according to the algorithm of image processing implemented for the detection and tracking of two different body regions. The first interface detects and tracks movements of the user's head, and these movements are transformed into linear and angular velocities in order to command a mobile robot. The second interface detects and tracks movements of the user's hand, and these movements are similarly transformed. In addition, this paper also presents the control laws for the robot. The experimental results demonstrate good performance and balance between complexity and feasibility for real-time applications.Fil: Pérez Berenguer, María Elisa. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Nasisi, Oscar Herminio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Rehabilitation robot cell for multimodal standing-up motion augmentation

The paper presents a robot cell for multimodal standing-up motion augmentation. The robot cell is aimed at augmenting the standing-up capabilities of impaired or paraplegic subjects. The setup incorporates the rehabilitation robot device, functional electrical stimulation system, measurement instrumentation and cognitive feedback system. For controlling the standing-up process a novel approach was developed integrating the voluntary activity of a person in the control scheme of the rehabilitation robot. The simulation results demonstrate the possibility of “patient-driven” robot-assisted standing-up training. Moreover, to extend the system capabilities, the audio cognitive feedback is aimed to guide the subject throughout rising. For the feedback generation a granular synthesis method is utilized displaying high-dimensional, dynamic data. The principle of operation and example sonification in standing-up are presented. In this manner, by integrating the cognitive feedback and “patient-driven” actuation systems, an effective motion augmentation system is proposed in which the motion coordination is under the voluntary control of the user

Real-time computation of distance to dynamic obstacles with multiple depth sensors

We present an efficient method to evaluate distances between dynamic obstacles and a number of points of interests (e.g., placed on the links of a robot) when using multiple depth cameras. A depth-space oriented discretization of the Cartesian space is introduced that represents at best the workspace monitored by a depth camera, including occluded points. A depth grid map can be initialized off line from the arrangement of the multiple depth cameras, and its peculiar search characteristics allows fusing on line the information given by the multiple sensors in a very simple and fast way. The real-time performance of the proposed approach is shown by means of collision avoidance experiments where two Kinect sensors monitor a human-robot coexistence task

Miniaturized modular manipulator design for high precision assembly and manipulation tasks

In this paper, design and control issues for the development of miniaturized manipulators which are aimed to be used in high precision assembly and manipulation tasks are presented. The developed manipulators are size adapted devices, miniaturized versions of conventional robots based on well-known kinematic structures. 3 degrees of freedom (DOF) delta robot and a 2 DOF pantograph mechanism enhanced with a rotational axis at the tip and a Z axis actuating the whole mechanism are given as examples of study. These parallel mechanisms are designed and developed to be used in modular assembly systems for the realization of high precision assembly and manipulation tasks. In that sense, modularity is addressed as an important design consideration. The design procedures are given in details in order to provide solutions for miniaturization and experimental results are given to show the achieved performances

Optical coherence tomography-based consensus definition for lamellar macular hole.

BackgroundA consensus on an optical coherence tomography definition of lamellar macular hole (LMH) and similar conditions is needed.MethodsThe panel reviewed relevant peer-reviewed literature to reach an accord on LMH definition and to differentiate LMH from other similar conditions.ResultsThe panel reached a consensus on the definition of three clinical entities: LMH, epiretinal membrane (ERM) foveoschisis and macular pseudohole (MPH). LMH definition is based on three mandatory criteria and three optional anatomical features. The three mandatory criteria are the presence of irregular foveal contour, the presence of a foveal cavity with undermined edges and the apparent loss of foveal tissue. Optional anatomical features include the presence of epiretinal proliferation, the presence of a central foveal bump and the disruption of the ellipsoid zone. ERM foveoschisis definition is based on two mandatory criteria: the presence of ERM and the presence of schisis at the level of Henle's fibre layer. Three optional anatomical features can also be present: the presence of microcystoid spaces in the inner nuclear layer (INL), an increase of retinal thickness and the presence of retinal wrinkling. MPH definition is based on three mandatory criteria and two optional anatomical features. Mandatory criteria include the presence of a foveal sparing ERM, the presence of a steepened foveal profile and an increased central retinal thickness. Optional anatomical features are the presence of microcystoid spaces in the INL and a normal retinal thickness.ConclusionsThe use of the proposed definitions may provide uniform language for clinicians and future research