53,590 research outputs found
Invariant EKF Design for Scan Matching-aided Localization
Localization in indoor environments is a technique which estimates the
robot's pose by fusing data from onboard motion sensors with readings of the
environment, in our case obtained by scan matching point clouds captured by a
low-cost Kinect depth camera. We develop both an Invariant Extended Kalman
Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based
solution to this problem. The two designs are successfully validated in
experiments and demonstrate the advantage of the IEKF design
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
The Phoenix Drone: An Open-Source Dual-Rotor Tail-Sitter Platform for Research and Education
In this paper, we introduce the Phoenix drone: the first completely
open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a
highly versatile, dual-rotor design and is engineered to be low-cost and easily
extensible/modifiable. Our open-source release includes all of the design
documents, software resources, and simulation tools needed to build and fly a
high-performance tail-sitter for research and educational purposes. The drone
has been developed for precision flight with a high degree of control
authority. Our design methodology included extensive testing and
characterization of the aerodynamic properties of the vehicle. The platform
incorporates many off-the-shelf components and 3D-printed parts, in order to
keep the cost down. Nonetheless, the paper includes results from flight trials
which demonstrate that the vehicle is capable of very stable hovering and
accurate trajectory tracking. Our hope is that the open-source Phoenix
reference design will be useful to both researchers and educators. In
particular, the details in this paper and the available open-source materials
should enable learners to gain an understanding of aerodynamics, flight
control, state estimation, software design, and simulation, while experimenting
with a unique aerial robot.Comment: In Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA'19), Montreal, Canada, May 20-24, 201
UAV/UGV Autonomous Cooperation: UAV Assists UGV to Climb a Cliff by Attaching a Tether
This paper proposes a novel cooperative system for an Unmanned Aerial Vehicle
(UAV) and an Unmanned Ground Vehicle (UGV) which utilizes the UAV not only as a
flying sensor but also as a tether attachment device. Two robots are connected
with a tether, allowing the UAV to anchor the tether to a structure located at
the top of a steep terrain, impossible to reach for UGVs. Thus, enhancing the
poor traversability of the UGV by not only providing a wider range of scanning
and mapping from the air, but also by allowing the UGV to climb steep terrains
with the winding of the tether. In addition, we present an autonomous framework
for the collaborative navigation and tether attachment in an unknown
environment. The UAV employs visual inertial navigation with 3D voxel mapping
and obstacle avoidance planning. The UGV makes use of the voxel map and
generates an elevation map to execute path planning based on a traversability
analysis. Furthermore, we compared the pros and cons of possible methods for
the tether anchoring from multiple points of view. To increase the probability
of successful anchoring, we evaluated the anchoring strategy with an
experiment. Finally, the feasibility and capability of our proposed system were
demonstrated by an autonomous mission experiment in the field with an obstacle
and a cliff.Comment: 7 pages, 8 figures, accepted to 2019 International Conference on
Robotics & Automation. Video: https://youtu.be/UzTT8Ckjz1
An Effective Multi-Cue Positioning System for Agricultural Robotics
The self-localization capability is a crucial component for Unmanned Ground
Vehicles (UGV) in farming applications. Approaches based solely on visual cues
or on low-cost GPS are easily prone to fail in such scenarios. In this paper,
we present a robust and accurate 3D global pose estimation framework, designed
to take full advantage of heterogeneous sensory data. By modeling the pose
estimation problem as a pose graph optimization, our approach simultaneously
mitigates the cumulative drift introduced by motion estimation systems (wheel
odometry, visual odometry, ...), and the noise introduced by raw GPS readings.
Along with a suitable motion model, our system also integrates two additional
types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random
Field assumption. We demonstrate how using these additional cues substantially
reduces the error along the altitude axis and, moreover, how this benefit
spreads to the other components of the state. We report exhaustive experiments
combining several sensor setups, showing accuracy improvements ranging from 37%
to 76% with respect to the exclusive use of a GPS sensor. We show that our
approach provides accurate results even if the GPS unexpectedly changes
positioning mode. The code of our system along with the acquired datasets are
released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters,
201
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