628 research outputs found
Development and Testing of a Software Framework for Controlling Humanoid Robots in Disaster-Response Scenarios
The aim of this thesis is to design and develop a modular software framework for controlling humanoid robots in teleoperation, in a context of disaster-response or civil defense. Over the years, natural (earthquakes, floods, etc.) or man-made disasters (nuclear reactor meltdowns, terrorist attacks, etc.) have caused several victims. The state of the art of disaster-robotics allows to deploy efficient and powerful robots in order to assist and support humans in the delicate phases of searching and rescuing survivors. In particular, with the use of teleoperation, the inclusion of a human operator (human-in-the-loop) can dramatically promote the application of humanoid robots, due to the human superior competence in critical thinking and context-awareness. This way, robots can be used as an interface between man and environment. Under these concepts, the thesis work focused on the design of a robust and efficient control architecture that brings whole-body locomotion and manipulation capabilities to the robot. Specifically, this thesis dealt with the development of a software module for teleoperating a robot while it is in a vehicle, making it able to drive. The module internal architecture is structured as a Finite State Machine, which allows to model a workflow of behaviors in an event-driven manner, providing safe and robust control in a teleoperation scenario. The effectiveness of the developed software has been validated during the DARPA Robotics Challenge Finals, occured in Pomona, CA (USA), on June 5-6 of 2015
An integrated dexterous robotic testbed for space applications
An integrated dexterous robotic system was developed as a testbed to evaluate various robotics technologies for advanced space applications. The system configuration consisted of a Utah/MIT Dexterous Hand, a PUMA 562 arm, a stereo vision system, and a multiprocessing computer control system. In addition to these major subsystems, a proximity sensing system was integrated with the Utah/MIT Hand to provide capability for non-contact sensing of a nearby object. A high-speed fiber-optic link was used to transmit digitized proximity sensor signals back to the multiprocessing control system. The hardware system was designed to satisfy the requirements for both teleoperated and autonomous operations. The software system was designed to exploit parallel processing capability, pursue functional modularity, incorporate artificial intelligence for robot control, allow high-level symbolic robot commands, maximize reusable code, minimize compilation requirements, and provide an interactive application development and debugging environment for the end users. An overview is presented of the system hardware and software configurations, and implementation is discussed of subsystem functions
In-home and remote use of robotic body surrogates by people with profound motor deficits
By controlling robots comparable to the human body, people with profound
motor deficits could potentially perform a variety of physical tasks for
themselves, improving their quality of life. The extent to which this is
achievable has been unclear due to the lack of suitable interfaces by which to
control robotic body surrogates and a dearth of studies involving substantial
numbers of people with profound motor deficits. We developed a novel, web-based
augmented reality interface that enables people with profound motor deficits to
remotely control a PR2 mobile manipulator from Willow Garage, which is a
human-scale, wheeled robot with two arms. We then conducted two studies to
investigate the use of robotic body surrogates. In the first study, 15 novice
users with profound motor deficits from across the United States controlled a
PR2 in Atlanta, GA to perform a modified Action Research Arm Test (ARAT) and a
simulated self-care task. Participants achieved clinically meaningful
improvements on the ARAT and 12 of 15 participants (80%) successfully completed
the simulated self-care task. Participants agreed that the robotic system was
easy to use, was useful, and would provide a meaningful improvement in their
lives. In the second study, one expert user with profound motor deficits had
free use of a PR2 in his home for seven days. He performed a variety of
self-care and household tasks, and also used the robot in novel ways. Taking
both studies together, our results suggest that people with profound motor
deficits can improve their quality of life using robotic body surrogates, and
that they can gain benefit with only low-level robot autonomy and without
invasive interfaces. However, methods to reduce the rate of errors and increase
operational speed merit further investigation.Comment: 43 Pages, 13 Figure
A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems
Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008–2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing
cost and better utilization of human resources. Robotic manipulator arms have
major role in the automation process. However, for complex manipulation tasks,
hard coding efficient and safe trajectories is challenging and time consuming.
Machine learning methods have the potential to learn such controllers based on
expert demonstrations. Despite promising advances, better approaches must be
developed to improve safety, reliability, and efficiency of ML methods in both
training and deployment phases. This survey aims to review cutting edge
technologies and recent trends on ML methods applied to real-world manipulation
tasks. After reviewing the related background on ML, the rest of the paper is
devoted to ML applications in different domains such as industry, healthcare,
agriculture, space, military, and search and rescue. The paper is closed with
important research directions for future works
Unmanned Ground Robots for Rescue Tasks
This chapter describes two unmanned ground vehicles that can help search and rescue teams in their difficult, but life-saving tasks. These robotic assets have been developed within the framework of the European project ICARUS. The large unmanned ground vehicle is intended to be a mobile base station. It is equipped with a powerful manipulator arm and can be used for debris removal, shoring operations, and remote structural operations (cutting, welding, hammering, etc.) on very rough terrain. The smaller unmanned ground vehicle is also equipped with an array of sensors, enabling it to search for victims inside semi-destroyed buildings. Working together with each other and the human search and rescue workers, these robotic assets form a powerful team, increasing the effectiveness of search and rescue operations, as proven by operational validation tests in collaboration with end users
Shared-Control Teleoperation Paradigms on a Soft Growing Robot Manipulator
Semi-autonomous telerobotic systems allow both humans and robots to exploit
their strengths, while enabling personalized execution of a task. However, for
new soft robots with degrees of freedom dissimilar to those of human operators,
it is unknown how the control of a task should be divided between the human and
robot. This work presents a set of interaction paradigms between a human and a
soft growing robot manipulator, and demonstrates them in both real and
simulated scenarios. The robot can grow and retract by eversion and inversion
of its tubular body, a property we exploit to implement interaction paradigms.
We implemented and tested six different paradigms of human-robot interaction,
beginning with full teleoperation and gradually adding automation to various
aspects of the task execution. All paradigms were demonstrated by two expert
and two naive operators. Results show that humans and the soft robot
manipulator can split control along degrees of freedom while acting
simultaneously. In the simple pick-and-place task studied in this work,
performance improves as the control is gradually given to the robot, because
the robot can correct certain human errors. However, human engagement and
enjoyment may be maximized when the task is at least partially shared. Finally,
when the human operator is assisted by haptic feedback based on soft robot
position errors, we observed that the improvement in performance is highly
dependent on the expertise of the human operator.Comment: 15 pages, 14 figure
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