2,454 research outputs found
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
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
GATSBI: An Online GTSP-Based Algorithm for Targeted Surface Bridge Inspection
We study the problem of visually inspecting the surface of a bridge using an
Unmanned Aerial Vehicle (UAV) for defects. We do not assume that the geometric
model of the bridge is known. The UAV is equipped with a LiDAR and RGB sensor
that is used to build a 3D semantic map of the environment. Our planner, termed
GATSBI, plans in an online fashion a path that is targeted towards inspecting
all points on the surface of the bridge. The input to GATSBI consists of a 3D
occupancy grid map of the part of the environment seen by the UAV so far. We
use semantic segmentation to segment the voxels into those that are part of the
bridge and the surroundings. Inspecting a bridge voxel requires the UAV to take
images from a desired viewing angle and distance. We then create a Generalized
Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints
for inspecting the bridge voxels and use an off-the-shelf GTSP solver to find
the optimal path for the given instance. As more parts of the environment are
seen, we replan the path. We evaluate the performance of our algorithm through
high-fidelity simulations conducted in Gazebo. We compare the performance of
this algorithm with a frontier exploration algorithm. Our evaluation reveals
that targeting the inspection to only the segmented bridge voxels and planning
carefully using a GTSP solver leads to more efficient inspection than the
baseline algorithms.Comment: 8 pages, 16 figure
- …