341 research outputs found
Cooperative UAVâUGV autonomous power pylon inspection: an investigation of cooperative outdoor vehicle positioning architecture
Realizing autonomous inspection, such as that of power distribution lines, through unmanned
aerial vehicle (UAV) systems is a key research domain in robotics. In particular, the use of autonomous
and semi-autonomous vehicles to execute the tasks of an inspection process can enhance the efficacy
and safety of the operation; however, many technical problems, such as those pertaining to the precise
positioning and path following of the vehicles, robust obstacle detection, and intelligent control, must be
addressed. In this study, an innovative architecture involving an unmanned aircraft vehicle (UAV) and an
unmanned ground vehicle (UGV) was examined for detailed inspections of power lines. In the proposed
strategy, each vehicle provides its position information to the other, which ensures a safe inspection
process. The results of real-world experiments indicate a satisfactory performance, thereby demonstrating
the feasibility of the proposed approach.This research was funded by National Counsel of Technological and Scientific Development of Brazil (CNPq).
The authors thank the National Counsel of Technological and Scientific Development of Brazil
(CNPq); Coordination for the Improvement of Higher Level People (CAPES); and the Brazilian Ministry of Science,
Technology, Innovation, and Communication (MCTIC). The authors would also like express their deepest gratitude
to Control Robotics for sharing the Pioneer P3 robot for the experiments. Thanks to Leticia Cantieri for editing the
experiment video.info:eu-repo/semantics/publishedVersio
Multisensor navigation systems: a remedy for GNSS vulnerabilities?
Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required
Dynamic Landing of an Autonomous Quadrotor on a Moving Platform in Turbulent Wind Conditions
Autonomous landing on a moving platform presents unique challenges for
multirotor vehicles, including the need to accurately localize the platform,
fast trajectory planning, and precise/robust control. Previous works studied
this problem but most lack explicit consideration of the wind disturbance,
which typically leads to slow descents onto the platform. This work presents a
fully autonomous vision-based system that addresses these limitations by
tightly coupling the localization, planning, and control, thereby enabling fast
and accurate landing on a moving platform. The platform's position,
orientation, and velocity are estimated by an extended Kalman filter using
simulated GPS measurements when the quadrotor-platform distance is large, and
by a visual fiducial system when the platform is nearby. The landing trajectory
is computed online using receding horizon control and is followed by a boundary
layer sliding controller that provides tracking performance guarantees in the
presence of unknown, but bounded, disturbances. To improve the performance, the
characteristics of the turbulent conditions are accounted for in the
controller. The landing trajectory is fast, direct, and does not require
hovering over the platform, as is typical of most state-of-the-art approaches.
Simulations and hardware experiments are presented to validate the robustness
of the approach.Comment: 7 pages, 8 figures, ICRA2020 accepted pape
MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems
This paper presents a modular autonomous Unmanned Aerial Vehicle (UAV)
platform called the Multi-robot Systems (MRS) Drone that can be used in a large
range of indoor and outdoor applications. The MRS Drone features unique
modularity with respect to changes in actuators, frames, and sensory
configuration. As the name suggests, the platform is specially tailored for
deployment within a MRS group. The MRS Drone contributes to the
state-of-the-art of UAV platforms by allowing smooth real-world deployment of
multiple aerial robots, as well as by outperforming other platforms with its
modularity. For real-world multi-robot deployment in various applications, the
platform is easy to both assemble and modify. Moreover, it is accompanied by a
realistic simulator to enable safe pre-flight testing and a smooth transition
to complex real-world experiments. In this manuscript, we present mechanical
and electrical designs, software architecture, and technical specifications to
build a fully autonomous multi UAV system. Finally, we demonstrate the full
capabilities and the unique modularity of the MRS Drone in various real-world
applications that required a diverse range of platform configurations.Comment: 49 pages, 39 figures, accepted for publication to the Journal of
Intelligent & Robotic System
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