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

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

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