17 research outputs found
Heuristic-based Incremental Probabilistic Roadmap for Efficient UAV Exploration in Dynamic Environments
Autonomous exploration in dynamic environments necessitates a planner that
can proactively respond to changes and make efficient and safe decisions for
robots. Although plenty of sampling-based works have shown success in exploring
static environments, their inherent sampling randomness and limited utilization
of previous samples often result in sub-optimal exploration efficiency.
Additionally, most of these methods struggle with efficient replanning and
collision avoidance in dynamic settings. To overcome these limitations, we
propose the Heuristic-based Incremental Probabilistic Roadmap Exploration
(HIRE) planner for UAVs exploring dynamic environments. The proposed planner
adopts an incremental sampling strategy based on the probabilistic roadmap
constructed by heuristic sampling toward the unexplored region next to the free
space, defined as the heuristic frontier regions. The heuristic frontier
regions are detected by applying a lightweight vision-based method to the
different levels of the occupancy map. Moreover, our dynamic module ensures
that the planner dynamically updates roadmap information based on the
environment changes and avoids dynamic obstacles. Simulation and physical
experiments prove that our planner can efficiently and safely explore dynamic
environments
Low computational-cost detection and tracking of dynamic obstacles for mobile robots with RGB-D cameras
Deploying autonomous robots in crowded indoor environments usually requires
them to have accurate dynamic obstacle perception. Although plenty of previous
works in the autonomous driving field have investigated the 3D object detection
problem, the usage of dense point clouds from a heavy LiDAR and their high
computation cost for learning-based data processing make those methods not
applicable to small robots, such as vision-based UAVs with small onboard
computers. To address this issue, we propose a lightweight 3D dynamic obstacle
detection and tracking (DODT) method based on an RGB-D camera, which is
designed for low-power robots with limited computing power. Our method adopts a
novel ensemble detection strategy, combining multiple computationally efficient
but low-accuracy detectors to achieve real-time high-accuracy obstacle
detection. Besides, we introduce a new feature-based data association method to
prevent mismatches and use the Kalman filter with the constant acceleration
model to track detected obstacles. In addition, our system includes an optional
and auxiliary learning-based module to enhance the obstacle detection range and
dynamic obstacle identification. The users can determine whether or not to run
this module based on the available computation resources. The proposed method
is implemented in a small quadcopter, and the experiments prove that the
algorithm can make the robot detect dynamic obstacles and navigate dynamic
environments safely.Comment: 8 pages, 12 figures, 2 table
Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization
Navigating dynamic environments requires the robot to generate collision-free
trajectories and actively avoid moving obstacles. Most previous works designed
path planning algorithms based on one single map representation, such as the
geometric, occupancy, or ESDF map. Although they have shown success in static
environments, due to the limitation of map representation, those methods cannot
reliably handle static and dynamic obstacles simultaneously. To address the
problem, this paper proposes a gradient-based B-spline trajectory optimization
algorithm utilizing the robot's onboard vision. The depth vision enables the
robot to track and represent dynamic objects geometrically based on the voxel
map. The proposed optimization first adopts the circle-based guide-point
algorithm to approximate the costs and gradients for avoiding static obstacles.
Then, with the vision-detected moving objects, our receding-horizon distance
field is simultaneously used to prevent dynamic collisions. Finally, the
iterative re-guide strategy is applied to generate the collision-free
trajectory. The simulation and physical experiments prove that our method can
run in real-time to navigate dynamic environments safely. Our software is
available on GitHub as an open-source package
A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles
Tunnel construction using the drill-and-blast method requires the 3D
measurement of the excavation front to evaluate underbreak locations.
Considering the inspection and measurement task's safety, cost, and efficiency,
deploying lightweight autonomous robots, such as unmanned aerial vehicles
(UAV), becomes more necessary and popular. Most of the previous works use a
prior map for inspection viewpoint determination and do not consider dynamic
obstacles. To maximally increase the level of autonomy, this paper proposes a
vision-based UAV inspection framework for dynamic tunnel environments without
using a prior map. Our approach utilizes a hierarchical planning scheme,
decomposing the inspection problem into different levels. The high-level
decision maker first determines the task for the robot and generates the target
point. Then, the mid-level path planner finds the waypoint path and optimizes
the collision-free static trajectory. Finally, the static trajectory will be
fed into the low-level local planner to avoid dynamic obstacles and navigate to
the target point. Besides, our framework contains a novel dynamic map module
that can simultaneously track dynamic obstacles and represent static obstacles
based on an RGB-D camera. After inspection, the Structure-from-Motion (SfM)
pipeline is applied to generate the 3D shape of the target. To our best
knowledge, this is the first time autonomous inspection has been realized in
unknown and dynamic tunnel environments. Our flight experiments in a real
tunnel prove that our method can autonomously inspect the tunnel excavation
front surface.Comment: 8 pages, 8 figure
A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera
The real-time dynamic environment perception has become vital for autonomous
robots in crowded spaces. Although the popular voxel-based mapping methods can
efficiently represent 3D obstacles with arbitrarily complex shapes, they can
hardly distinguish between static and dynamic obstacles, leading to the limited
performance of obstacle avoidance. While plenty of sophisticated learning-based
dynamic obstacle detection algorithms exist in autonomous driving, the
quadcopter's limited computation resources cannot achieve real-time performance
using those approaches. To address these issues, we propose a real-time dynamic
obstacle tracking and mapping system for quadcopter obstacle avoidance using an
RGB-D camera. The proposed system first utilizes a depth image with an
occupancy voxel map to generate potential dynamic obstacle regions as
proposals. With the obstacle region proposals, the Kalman filter and our
continuity filter are applied to track each dynamic obstacle. Finally, the
environment-aware trajectory prediction method is proposed based on the Markov
chain using the states of tracked dynamic obstacles. We implemented the
proposed system with our custom quadcopter and navigation planner. The
simulation and physical experiments show that our methods can successfully
track and represent obstacles in dynamic environments in real-time and safely
avoid obstacles
Climatology of Different Classifications of Tropical Cyclones Landfalling in Guangdong Province of China during 1951–2020
The climatology of different classifications (based on the intensity at the landfall time) of tropical cyclones (TCs) making landfall in Guangdong Province of China during 1951–2020 (70 years) is investigated using the best track data from the China Meteorological Administration and ERA5 reanalysis data. There were 234 TCs making landfall in Guangdong Province, with more severe tropical storms (STSs, 30.8%) and typhoons (TYs, 27.3%), and less tropical depressions (TDs, 19.7%) and tropical storms (TSs, 22.2%) during the past 70 years. The frequency of the landfall TCs had a significant interannual oscillation of 2–5 years. Landfall TCs generated over the western North Pacific (WNP) were usually more and stronger than those generated over the South China Sea (SCS). The TCs generated over the WNP had longer lifetime duration and shorter on-land duration than those generated over the SCS. TCs making landfall in western Guangdong were the most, followed by central Guangdong and eastern Guangdong. The composite analysis using TC-relative coordinates indicated that the precipitation of different classifications of TCs making landfall in Guangdong Province was asymmetric, which was stronger in the south of the TC center. The position of the maximum precipitation showed a cyclonic rotation around the TC center with increasing TC intensity. Generally, the vertical velocity, moisture flux, warm core, and vertical wind shear enhanced with the increasing landfall TC intensity. The vertical velocity and moisture flux of different classifications of TCs also showed an asymmetric structure related to the distribution of TC precipitation. TSs, STSs, and TYs had a double warm-core configuration. The precipitation of the TDs and TSs usually occurred over the down-shear of average vertical wind shear, those of the STSs and TYs over the left-of-shear