Human Management of the Hierarchical System for the Control of Multiple Mobile Robots

In order to take advantage of autonomous robotic systems, and yet ensure successful completion of all feasible tasks, we propose a mediation hierarchy in which an operator can interact at all system levels. Robotic systems are not robust in handling un-modeled events. Reactive behaviors may be able to guide the robot back into a modeled state and to continue. Reasoning systems may simply fail. Once a system has failed it is difficult to re-start the task from the failed state. Rather, the rule base is revised, programs altered, and the task re-tried from the beginning

Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes

We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user's goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seamlessly adapt to reach the user's demonstrated goal. For scene perception, we propose the Discriminatively-Informed Generative Estimation of Scenes and Transforms (DIGEST) method to infer the initial and goal states of the world from RGBD images. The efficacy of SRP with DIGEST perception is demonstrated for the task of tray-setting with a Michigan Progress Fetch robot. Scene perception and task execution are evaluated with a public household occlusion dataset and our cluttered scene dataset.Comment: published in ICRA 201

A Multiagent System for Intelligent Material Handling

The goal of our research is to investigate manipulation, mobility, sensing, control and coordination for a multiagent robotic system employed in the task of material handling, in an unstructured, indoor environment. In this research, manipulators, observers, vehicles, sensors, and human operator(s) are considered to be agents. Alternatively, an agent can be a general-purpose agent (for example, a six degree of freedom manipulator on a mobile platform with visual force, touch and position sensors). Possible applications for such a system includes handling of waste and hazardous materials, decontamination of nuclear plants, and interfacing between special purpose material handling devices in warehouses. The fundamental research problems that will be studied are organization, or the decomposition of the task into subtasks and configuring the multiple agents with appropriate human interaction, exploration, or the process of exploring geometric, material and other properties about the environment and other agents, and coordination, or the dynamic control of multiple agents for manipulation and transportation of objects to a desired destination

Telerobotic Sensor-based Tool Control Derived From Behavior-based Robotics Concepts

@font-face { font-family: TimesNewRoman ; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0in 0in 0.0001pt; font-size: 12pt; font-family: Times New Roman ; }div.Section1 { page: Section1; } Teleoperated task execution for hazardous environments is slow and requires highly skilled operators. Attempts to implement telerobotic assists to improve efficiency have been demonstrated in constrained laboratory environments but are not being used in the field because they are not appropriate for use on actual remote systems operating in complex unstructured environments using typical operators. This work describes a methodology for combining select concepts from behavior-based systems with telerobotic tool control in a way that is compatible with existing manipulator architectures used by remote systems typical to operations in hazardous environment. The purpose of the approach is to minimize the task instance modeling in favor of a priori task type models while using sensor information to register the task type model to the task instance. The concept was demonstrated for two tools useful to decontamination & dismantlement type operations—a reciprocating saw and a powered socket tool. The experimental results demonstrated that the approach works to facilitate traded control telerobotic tooling execution by enabling difficult tasks and by limiting tool damage. The role of the tools and tasks as drivers to the telerobotic implementation was better understood in the need for thorough task decomposition and the discovery and examination of the tool process signature. The contributions of this work include: (1) the exploration and evaluation of select features of behavior-based robotics to create a new methodology for integrating telerobotic tool control with positional teleoperation in the execution of complex tool-centric remote tasks, (2) the simplification of task decomposition and the implementation of sensor-based tool control in such a way that eliminates the need for the creation of a task instance model for telerobotic task execution, and (3) the discovery, demonstrated use, and documentation of characteristic tool process signatures that have general value in the investigation of other tool control, tool maintenance, and tool development strategies above and beyond the benefit sustained for the methodology described in this work

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Remote Control of Mobile Robot using the Virtual Reality

In this paper we present the simulation and manipulation of  teleoperation system for remote control of mobile robot using the Virtual Reality (VR). The objective of this work is to allow the operator to control and supervise a unicycle type mobile robot. In this research we followed three ways: The use of articulated robotic mobile on the Web, the design of remote environment for the experimentation using the network for the mobile robot and the  architecture of control is proposed to facilitate the piloting of the robot. This work proposes a hardware and software architecture based on communication and information technologies to control the virtual robot to improve the control towards the remote robot. A path planning method is integrated to the remote control system. Results show the real possibilities offered by this manipulation, in order to follow a trajectory of the robot and to create applications with a distance access to facilities through networks like the Internet and wireless

Approach to Adapt a Legacy Manufacturing System Into the IoT Paradigm

This work has been supported by Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, by Uninova-CTS research unit and by national funds through FCT -Fundação para a Ciência e a Tecnologia within the research unit CTS - Centro de Tecnologia e Sistemas (project UID/EEA/00066/2013). The authors would like to thank all the institutions.Enterprises are adopting the Internet of Things paradigm as a strategy to improve competitiveness. But enterprises also need to rely on their legacy systems, which are of vital importance to them and normally difficult to reconfigure or modify, their mere replacement being usually not affordable. These systems constitute, therefore, barriers to agility and competitiveness, raising the need to develop cost-effective ways for IoT adaptation. An approach for adapting legacy manufacturing systems into the IoT realm is proposed in this research. The methodology is twofold: an adaptation board is firstly designed to provide IoT connectivity, allowing to remotely invoke the “legacy” functionality as services. Then, the board itself can leverage the legacy system by developing additional functionalities inside it, as the update process is usually triggered by the need of new functionality from these systems. An experiment, which consists of adapting to IoT a small distribution line that is controlled by an aged Programmable Logic Controller, is developed to illustrate how straightforward, affordable and cost effective the adaptation approach is, allowing to holistically achieve a new system with more sophisticated functionality.publishersversionpublishe

\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay

I-Rescue: A Coalition Based System to Support Disaster Relief Operations

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.I-Rescue is a research programme that aims to develop knowledge-based tools for disaster relief domains. One important aspect of the I-Rescue development is to highlight the requirements regarding the collaborative activities of planning and execution, considering a hierarchical structure of decision-making and a mixed initiative style of interaction between users and systems. This paper discusses the design and implementation of I-Rescue and its use in a search and rescue domain where joint users, assisted by customised agents, are able to perform complementary tasks