26,346 research outputs found
Transfer Learning-Based Crack Detection by Autonomous UAVs
Unmanned Aerial Vehicles (UAVs) have recently shown great performance
collecting visual data through autonomous exploration and mapping in building
inspection. Yet, the number of studies is limited considering the post
processing of the data and its integration with autonomous UAVs. These will
enable huge steps onward into full automation of building inspection. In this
regard, this work presents a decision making tool for revisiting tasks in
visual building inspection by autonomous UAVs. The tool is an implementation of
fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack
detection. It offers an optional mechanism for task planning of revisiting
pinpoint locations during inspection. It is integrated to a quadrotor UAV
system that can autonomously navigate in GPS-denied environments. The UAV is
equipped with onboard sensors and computers for autonomous localization,
mapping and motion planning. The integrated system is tested through
simulations and real-world experiments. The results show that the system
achieves crack detection and autonomous navigation in GPS-denied environments
for building inspection
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured
industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined
well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure
a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and
a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system
is that all the algorithms relied on the multi-resolution of the octomap for the world representation.
We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements
of the capability of the open-source system to run online and on-board the UAV in real-time. Our
approach is compared to different reference heuristics under this simulation environment showing
better performance in regards to the amount of explored space. With the proposed approach, the UAV
is able to explore 93% of the search space under 30 min, generating a path without repetition that
adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Information gathering algorithms play a key role in unlocking the potential
of robots for efficient data collection in a wide range of applications.
However, most existing strategies neglect the fundamental problem of the robot
pose uncertainty, which is an implicit requirement for creating robust,
high-quality maps. To address this issue, we introduce an informative planning
framework for active mapping that explicitly accounts for the pose uncertainty
in both the mapping and planning tasks. Our strategy exploits a Gaussian
Process (GP) model to capture a target environmental field given the
uncertainty on its inputs. For planning, we formulate a new utility function
that couples the localization and field mapping objectives in GP-based mapping
scenarios in a principled way, without relying on any manually tuned
parameters. Extensive simulations show that our approach outperforms existing
strategies, with reductions in mean pose uncertainty and map error. We also
present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation
Letters (and IEEE International Conference on Robotics and Automation
Efficient Autonomous Navigation for Planetary Rovers with Limited Resources
Rovers operating on Mars are in need of more and more autonomous features to ful ll their
challenging mission requirements. However, the inherent constraints of space systems make
the implementation of complex algorithms an expensive and difficult task. In this paper
we propose a control architecture for autonomous navigation. Efficient implementations of
autonomous features are built on top of the current ExoMars navigation method, enhancing
the safety and traversing capabilities of the rover. These features allow the rover to detect
and avoid hazards and perform long traverses by following a roughly safe path planned by
operators on ground. The control architecture implementing the proposed navigation mode
has been tested during a field test campaign on a planetary analogue terrain. The experiments
evaluated the proposed approach, autonomously completing two long traverses while
avoiding hazards. The approach only relies on the optical Localization Cameras stereobench,
a sensor that is found in all rovers launched so far, and potentially allows for computationally
inexpensive long-range autonomous navigation in terrains of medium difficulty
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
- …