Sensor Augmented Virtual Reality Based Teleoperation Using Mixed Autonomy

A multimodal teleoperation interface is introduced, featuring an integrated virtual reality (VR) based simulation augmented by sensors and image processing capabilities onboard the remotely operated vehicle. The proposed virtual reality interface fuses an existing VR model with live video feed and prediction states, thereby creating a multimodal control interface. VR addresses the typical limitations of video based teleoperation caused by signal lag and limited field of view, allowing the operator to navigate in a continuous fashion. The vehicle incorporates an onboard computer and a stereo vision system to facilitate obstacle detection. A vehicle adaptation system with a priori risk maps and a real-state tracking system enable temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle’s teleoperated state. The system provides real time update of the virtual environment based on anomalies encountered by the vehicle. The VR based multimodal teleoperation interface is expected to be more adaptable and intuitive when compared with other interfaces

a robotized projective interface for human robot learning scenarios

Abstract In this work we discuss a novel robotics interface with perception and projection capabilities for facilitating the skill transfer process. The interface aims at allowing humans and robots to interact with each other in the same environment, with respect to visual feedback. During the learning process, the real workspace can be used as a graphical interface for helping the user to better understand what the robot has learned up to then, to display information about the task or to get feedback and guidance. Thus, the user can incrementally visualize and assess the learner's state and, at the same time, focus on the skill transfer without disrupting the continuity of the teaching interaction. We also propose a proof-of-concept, as a core element of the architecture, based on an experimental setting where a picoprojector and an rgb-depth sensor are mounted onto the end-effector of a 7-DOF robotic arm

High-Speed Vision and Force Feedback for Motion-Controlled Industrial Manipulators

Over the last decades, both force sensors and cameras have emerged as useful sensors for different applications in robotics. This thesis considers a number of dynamic visual tracking and control problems, as well as the integration of these techniques with contact force control. Different topics ranging from basic theory to system implementation and applications are treated. A new interface developed for external sensor control is presented, designed by making non-intrusive extensions to a standard industrial robot control system. The structure of these extensions are presented, the system properties are modeled and experimentally verified, and results from force-controlled stub grinding and deburring experiments are presented. A novel system for force-controlled drilling using a standard industrial robot is also demonstrated. The solution is based on the use of force feedback to control the contact forces and the sliding motions of the pressure foot, which would otherwise occur during the drilling phase. Basic methods for feature-based tracking and servoing are presented, together with an extension for constrained motion estimation based on a dual quaternion pose parametrization. A method for multi-camera real-time rigid body tracking with time constraints is also presented, based on an optimal selection of the measured features. The developed tracking methods are used as the basis for two different approaches to vision/force control, which are illustrated in experiments. Intensity-based techniques for tracking and vision-based control are also developed. A dynamic visual tracking technique based directly on the image intensity measurements is presented, together with new stability-based methods suitable for dynamic tracking and feedback problems. The stability-based methods outperform the previous methods in many situations, as shown in simulations and experiments

Discrete event simulation and virtual reality use in industry: new opportunities and future trends

This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within industry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This paper reviews active research topics such as VR and DES real-time integration, communication protocols, system design considerations, model validation, and applications of VR and DES. While summarizing future research directions for this technology combination, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization requirements of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recognition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets