7,557 research outputs found
RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously
navigate through, identify, and reach areas of interest; and there recognize,
localize, and manipulate work tools to perform complex manipulation tasks. The
proposed contribution includes a modular software architecture where each
module solves specific sub-tasks and that can be easily enlarged to satisfy new
requirements. Included indoor and outdoor tests demonstrate the capability of
the proposed system to autonomously detect a target object (a panel) and
precisely dock in front of it while avoiding obstacles. They show it can
autonomously recognize and manipulate target work tools (i.e., wrenches and
valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve
stem). A specific case study is described where the proposed modular
architecture lets easy switch to a semi-teleoperated mode. The paper
exhaustively describes description of both the hardware and software setup of
RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International
Robotics Challenge, and the lessons we learned when participating at this
competition, where we ranked third in the Gran Challenge in collaboration with
the Czech Technical University in Prague, the University of Pennsylvania, and
the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics,
published by Taylor & Franci
Al-Robotics team: A cooperative multi-unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge
The Al-Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al-Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.Unión Europea H2020 73166
Carnegie Mellon Team Tartan: Mission-level Robustness with Rapidly Deployed Autonomous Aerial Vehicles in the MBZIRC 2020
For robotics systems to be used in high risk, real-world situations, they
have to be quickly deployable and robust to environmental changes,
under-performing hardware, and mission subtask failures. Robots are often
designed to consider a single sequence of mission events, with complex
algorithms lowering individual subtask failure rates under some critical
constraints. Our approach is to leverage common techniques in vision and
control and encode robustness into mission structure through outcome monitoring
and recovery strategies, aided by a system infrastructure that allows for quick
mission deployments under tight time constraints and no central communication.
We also detail lessons in rapid field robotics development and testing. Systems
were developed and evaluated through real-robot experiments at an outdoor test
site in Pittsburgh, Pennsylvania, USA, as well as in the 2020 Mohamed Bin Zayed
International Robotics Challenge. All competition trials were completed in
fully autonomous mode without RTK-GPS. Our system led to 4th place in Challenge
2 and 7th place in the Grand Challenge, and achievements like popping five
balloons (Challenge 1), successfully picking and placing a block (Challenge 2),
and dispensing the most water autonomously with a UAV of all teams onto an
outdoor, real fire (Challenge 3).Comment: 28 pages, 26 figures. To appear in Field Robotics, Special Issues on
MBZIRC 202
Fifteen Years of Chandra Operation: Scientific Highlights and Lessons Learned
NASA's Chandra X-Ray Observatory, designed for three years of operation with a goal of five years is now entering its 15-th year of operation. Thanks to its superb angular resolution, the Observatory continues to yield new and exciting results, many of which were totally unanticipated prior to launch. We discuss the current technical status, review recent scientific highlights, indicate a few future directions, and present what we feel is the most important lesson learned from our experience of building and operating this great observatory
Astrometry with the Keck-Interferometer: the ASTRA project and its science
The sensitivity and astrometry upgrade ASTRA of the Keck Interferometer is
introduced. After a brief overview of the underlying interferometric
principles, the technology and concepts of the upgrade are presented. The
interferometric dual-field technology of ASTRA will provide the KI with the
means to observe two objects simultaneously, and measure the distance between
them with a precision eventually better than 100 uas. This astrometric
functionality of ASTRA will add a unique observing tool to fields of
astrophysical research as diverse as exo-planetary kinematics, binary
astrometry, and the investigation of stars accelerated by the massive black
hole in the center of the Milky Way as discussed in this contribution.Comment: 22 pages, 10 figures (low resolution), contribution to the
summerschool "Astrometry and Imaging with the Very Large Telescope
Interferometer", 2 - 13 June, 2008, Keszthely, Hungary, corrected authorlis
Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops
The US apple industry relies heavily on semi-skilled manual labor force for
essential field operations such as training, pruning, blossom and green fruit
thinning, and harvesting. Blossom thinning is one of the crucial crop load
management practices to achieve desired crop load, fruit quality, and return
bloom. While several techniques such as chemical, and mechanical thinning are
available for large-scale blossom thinning such approaches often yield
unpredictable thinning results and may cause damage the canopy, spurs, and leaf
tissue. Hence, growers still depend on laborious, labor intensive and expensive
manual hand blossom thinning for desired thinning outcomes. This research
presents a robotic solution for blossom thinning in apple orchards using a
computer vision system with artificial intelligence, a six degrees of freedom
robotic manipulator, and an electrically actuated miniature end-effector for
robotic blossom thinning. The integrated robotic system was evaluated in a
commercial apple orchard which showed promising results for targeted and
selective blossom thinning. Two thinning approaches, center and boundary
thinning, were investigated to evaluate the system ability to remove varying
proportion of flowers from apple flower clusters. During boundary thinning the
end effector was actuated around the cluster boundary while center thinning
involved end-effector actuation only at the cluster centroid for a fixed
duration of 2 seconds. The boundary thinning approach thinned 67.2% of flowers
from the targeted clusters with a cycle time of 9.0 seconds per cluster,
whereas center thinning approach thinned 59.4% of flowers with a cycle time of
7.2 seconds per cluster. When commercially adopted, the proposed system could
help address problems faced by apple growers with current hand, chemical, and
mechanical blossom thinning approaches
- …