274 research outputs found
Sampling-Based Motion Planning: A Comparative Review
Sampling-based motion planning is one of the fundamental paradigms to
generate robot motions, and a cornerstone of robotics research. This
comparative review provides an up-to-date guideline and reference manual for
the use of sampling-based motion planning algorithms. This includes a history
of motion planning, an overview about the most successful planners, and a
discussion on their properties. It is also shown how planners can handle
special cases and how extensions of motion planning can be accommodated. To put
sampling-based motion planning into a larger context, a discussion of
alternative motion generation frameworks is presented which highlights their
respective differences to sampling-based motion planning. Finally, a set of
sampling-based motion planners are compared on 24 challenging planning
problems. This evaluation gives insights into which planners perform well in
which situations and where future research would be required. This comparative
review thereby provides not only a useful reference manual for researchers in
the field, but also a guideline for practitioners to make informed algorithmic
decisions.Comment: 25 pages, 7 figures, Accepted for Volume 7 (2024) of the Annual
Review of Control, Robotics, and Autonomous System
Angle-Encoded Swarm Optimization for UAV Formation Path Planning
© 2018 IEEE. This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (DAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of DAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angle-encoded Particle Swarm Optimization (θ- PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of- Things (loT) for multi-directional transmission and reception of data between the DAV s, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach
Feasibility study of beam-expanding telescopes in the interferometer arms for the Einstein Telescope
The optical design of the Einstein Telescope (ET) is based on a dual-recycled
Michelson interferometer with Fabry-Perot cavities in the arms. ET will be
constructed in a new infrastructure, allowing us to consider different
technical implementations beyond the constraints of the current facilities. In
this paper we investigate the feasibility of using beam-expander telescopes in
the interferometer arms. We provide an example implementation that matches the
optical layout as presented in the ET design update 2020. We further show that
the beam-expander telescopes can be tuned to compensate for mode mismatches
between the arm cavities and the rest of the interferometer.Comment: 9 pages with 11 figures. To be published in: PR
Improving Sampling-Based Motion Planning Using Library of Trajectories
PlánovánĂ pohybu je jednĂm z podstatnĂ˝ch problĂ©mĹŻ robotiky. Tato práce kombinuje pokroky v plánovánĂ pohybu a hodnocenĂ podobnosti objektĹŻ za účelem zrychlenĂ plánovánĂ ve statickĂ˝ch prostĹ™edĂch. Prvnà část tĂ©to práce pojednává o souÄŤasnĂ˝ch metodách pouĹľĂvanĂ˝ch pro hodnocenĂ podobnosti objektĹŻ a plánovánĂ pohybu. ProstĹ™ednà část popisuje, jak jsou tyto metody pouĹľity pro zrychlenĂ plánovánĂ s vyuĹľitĂm zĂskanĂ˝ch znalostĂ o prostĹ™edĂ. V poslednà části jsou navrĹľenĂ© metody porovnány s ostatnĂmi plánovaÄŤi v nezávislĂ©m testu. Námi navrĹľenĂ© algoritmy se v experimentech ukázaly bĂ˝t ÄŤasto rychlejšà v porovnánĂ s ostatnĂmi plánovaÄŤi. TakĂ© ÄŤasto nacházely cesty v prostĹ™edĂch, kde ostatnĂ plánovaÄŤe nebyly schopny cestu nalĂ©zt.Motion planning is one of the fundamental problems in robotics. This thesis combines the advances in motion planning and shape matching to improve planning speeds in static environments. The first part of this thesis covers current methods used in object similarity evaluation and motion planning. The middle part describes how these methods are used together to improve planning speeds by utilizing prior knowledge about the environment, along with additional modifications. In the last part, the proposed methods are tested against other state-of-the-art planners in an independent benchmarking facility. The proposed algorithms are shown to be faster than other planners in many cases, often finding paths in environments where the other planners are unable to
A framework for roadmap-based navigation and sector-based localization of mobile robots
Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator
Coordinated Parallel Runway Approaches
The current air traffic environment in airport terminal areas experiences substantial delays when weather conditions deteriorate to Instrument Meteorological Conditions (IMC). Expected future increases in air traffic will put additional pressures on the National Airspace System (NAS) and will further compound the high costs associated with airport delays. To address this problem, NASA has embarked on a program to address Terminal Area Productivity (TAP). The goals of the TAP program are to provide increased efficiencies in air traffic during the approach, landing, and surface operations in low-visibility conditions. The ultimate goal is to achieve efficiencies of terminal area flight operations commensurate with Visual Meteorological Conditions (VMC) at current or improved levels of safety
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