Advancing automation and robotics technology for the space station and for the US economy: Submitted to the United States Congress October 1, 1987

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the Law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the fifth in a series of progress updates and covers the period between 16 May 1987 and 30 September 1987. NASA has accepted the basic recommendations of ATAC for its space station efforts. ATAC and NASA agree that the mandate of Congress is that an advanced automation and robotics technology be built to support an evolutionary space station program and serve as a highly visible stimulator affecting the long-term U.S. economy

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

Exploring Human Compliance Toward a Package Delivery Robot

Human-Robot Interaction (HRI) research on combat robots and autonomous carsdemonstrate faulty robots significantly decrease trust. However, HRI studies consistently show people overtrust domestic robots in households, emergency evacuation scenarios, and building security. This thesis presents how two theories, cognitive dissonance and selective attention, confound domestic HRI scenarios and uses the theory to design a novel HRI scenario with a package delivery robot in a public setting. Over 40 undergraduates were recruited within a university library to follow a package delivery robot to three stops, under the guise of “testing its navigation around people.” The second delivery was an open office which appeared private. Without labeling the packages, in 15 trials only 2 individuals entered the room at the second stop, whereas a pair of participants were much more likely to enter the room. Labeling the packages significantly increased the likelihood individuals would enter the office. The third stop was at the end of a long, isolated hallway blocked by a door marked “Emergency Exit Only. Alarm will Sound.” No one seriously thought about opening the door. Nonverbal robot prods such as waiting one minute or nudging the door were perceived as malfunctioning behavior. To demonstrate selective attention, a second route led to an emergency exit door in a public computer lab, with the intended destination an office several feet away. When the robot communicated with beeps only 45% of individuals noticed the emergency exit door. No one noticed the emergency exit door when the robot used speech commands, although its qualitative rating significantly improved. In conclusion, this thesis shows robots must make explicit requests to generate overtrust. Explicit interactions increase participant engagement with the robot, which increases selective attention towards their environment

Would You Obey an Aggressive Robot: A Human-Robot Interaction Field Study

© 2018 IEEE. Social Robots have the potential to be of tremendous utility in healthcare, search and rescue, surveillance, transport, and military applications. In many of these applications, social robots need to advise and direct humans to follow important instructions. In this paper, we present the results of a Human-Robot Interaction field experiment conducted using a PR2 robot to explore key factors involved in obedience of humans to social robots. This paper focuses on studying how the human degree of obedience to a robot's instructions is related to the perceived aggression and authority of the robot's behavior. We implemented several social cues to exhibit and convey both authority and aggressiveness in the robot's behavior. In addition to this, we also analyzed the impact of other factors such as perceived anthropomorphism, safety, intelligence and responsibility of the robot's behavior on participants' compliance with the robot's instructions. The results suggest that the degree of perceived aggression in the robot's behavior by different participants did not have a significant impact on their decision to follow the robot's instruction. We have provided possible explanations for our findings and identified new research questions that will help to understand the role of robot authority in human-robot interaction, and that can help to guide the design of robots that are required to provide advice and instructions

Force sensing enhancement of robot system

At present there is a general industrial need to improve robot performance. Force feedback, which involves sensing and actuation, is one means of improving the relative position between the workpiece and the end-effector. In this research work various causes of errors and poor robot performance are identified. Several methods of improving the performance of robotic systems are discussed. As a result of this research, a system was developed which is interposed between the wrist and the gripper of the manipulator. This system integrates a force sensor with a micro-manipulator, via an electronic control unit, with a micro-computer to enhance a robot system. The force sensor, the micromanipulator and the electronic control unit, were all designed and manufactured at the robotic centre of Middlesex Polytechnic. The force feedback is provided by means of strain gauges and the associated bridge circuitry. Control algorithms which define the relationship between the force detected and the motion required are implemented in the software. The software is capable of performing two specific tasks in real time, these are: 1- Inserting a peg into a hole 2- Following an unknown geometric path A rig was designed and manufactured to enable the robot to follow different geometric shapes and paths in which force control was achieved mainly by control of the micro-manipulator

Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

This paper presents features and advanced settings for a robot manipulator controller in a fully interconnected intelligent manufacturing system. Every system is made up of different agents. As also occurs in the Internet of Things and smart cities, the big issue here is to ensure not only that implementation is key, but also that there is better common understanding among the main players. The commitment of all agents is still required to translate that understanding into practice in Industry 4.0. Mutual interactions such as machine-to-machine and man-to-machine are solved in real time with cyber physical capabilities. This paper explores intelligent manufacturing through the context of industrial robot manipulators within a Smart Factory. An online communication algorithm with proven intelligent manufacturing abilities is proposed to solve real-time interactions. The algorithm is developed to manage and control all robot parameters in real-time. The proposed tool in conjunction with the intelligent manufacturing core incorporates data from the robot manipulators into the industrial big data to manage the factory. The novelty is a communication tool that implements the Industry 4.0 standards to allow communications among the required entities in the complete system. It is achieved by the developed tool and implemented in a real robot and simulation.This research was partially funded by the Ministry of Economy, Industry and Competitiveness in the project with reference RTC-2014-3070-5. In addition, the work has been partially funded by the project Strategic Action in Robotics, Computer Vision and Automation financed by University Carlos III of Madrid