Han Chen

Yang Yu, Han Chen

Structural Health Monitoring (SHM) plays an indispensable role in ensuring the longevity and safety of infrastructure. With the rapid growth of sensor technology, the volume of data generated from various structures has seen an unprecedented surge, bringing forth challenges in efficient analysis and interpretation. This paper introduces a novel deep learning algorithm tailored for the complexities inherent in multimodal vibration signals prevalent in SHM. By amalgamating convolutional and recurrent architectures, the algorithm adeptly captures both localized and prolonged structural behaviors. The pivotal integration of attention mechanisms further enhances the model's capability, allowing it to discern and prioritize salient structural responses from extraneous noise. Our results showcase significant improvements in predictive accuracy, early damage detection, and adaptability across multiple SHM scenarios. In light of the critical nature of SHM, the proposed approach not only offers a robust analytical tool but also paves the way for more transparent and interpretable AI-driven SHM solutions. Future prospects include real-time processing, integration with external environmental factors, and a deeper emphasis on model interpretability.

Han Chen, Peng Lu

Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a technique to distinguish dynamic obstacles from static ones with only point cloud input. Then, a computationally efficient obstacle avoidance motion planning approach is proposed and it is in line with an improved relative velocity method. The approach is able to avoid both static obstacles and dynamic ones in the same framework. For static and dynamic obstacles, the collision check and motion constraints are different, and they are integrated into one framework efficiently. In addition, we present several techniques to improve the algorithm performance and deal with the time gap between different submodules. The proposed approach is implemented to run onboard in real-time and validated extensively in simulation and hardware tests. Our average single step calculating time is less than 20 ms.

S. Chen, H. Chen, W. Zhou et al.

Visual Simultaneous Localization and Mapping (v-SLAM) and navigation of multirotor Unmanned Aerial Vehicles (UAV) in an unknown environment have grown in popularity for both research and education. However, due to the complex hardware setup, safety precautions, and battery constraints, extensive physical testing can be expensive and time-consuming. As an alternative solution, simulation tools lower the barrier to carry out the algorithm testing and validation before field trials. In this letter, we customize the ROS-Gazebo-PX4 simulator in deep and provide an end-to-end simulation solution for the UAV v-SLAM and navigation study. A set of localization, mapping, and path planning kits were also integrated into the simulation platform. In our simulation, various aspects, including complex environments and onboard sensors, can simultaneously interact with our navigation framework to achieve specific surveillance missions. In this end-to-end simulation, we achieved click and fly level autonomy UAV navigation. The source code is open to the research community.

Han Chen, Peng Lu

For accomplishing a variety of missions in challenging environments, the capability of navigating with full autonomy while avoiding unexpected obstacles is the most crucial requirement for UAVs in real applications. In this paper, we proposed such a computationally efficient obstacle avoidance trajectory planner that can be used in cluttered unknown environments. Because of the narrow view field of single depth camera on a UAV, the information of obstacles around is quite limited thus the shortest entire path is difficult to achieve. Therefore we focus on the time cost of the trajectory planner and safety rather than other factors. This planner is mainly composed of a point cloud processor, a waypoint publisher with Heuristic Angular Search(HAS) method and a motion planner with minimum acceleration optimization. Furthermore, we propose several techniques to enhance safety by making the possibility of finding a feasible trajectory as big as possible. The proposed approach is implemented to run onboard in real-time and is tested extensively in simulation and the average control output calculating time of iteration steps is less than 18 ms.