Index
Robot Movement Planning for Obstacle Avoidance using Reinforcement Learning
Obstacle avoidance for robotic arms is an important issue in robot control. Limited by factors such as equipment, cost, and labor, some application scenarios require the robot to have the ability to plan its own movement to reach the goal position or state. 
In real working environments, where obstacles’ properties are various, the traditional search algorithms are difficult to adapt to large-scale space and continuous action requirements. Artificial potential field (APF) method is a widely used obstacle avoidance path planning method, but it alsohas some shortcomings and will fall into local optimality in some special situations. Based on APF method, reinforcement learning (RL) can theoretically achieve optimization in continuous space. Combined with some modifications to traditional APF method, we define states, and actions and design the reward function, which is regarded as an important part of reinforcement learning, to form a motion planning agent in the
3D world, so that the robot end-effector is able to reach the goal position, emphasizing the avoidance of collision between obstacles and the whole robot arm.
Sinogram Analysis Using Attention U-Net: A Methodological Approach to Defect Detection and Localization in Parallel Beam Computed Tomography
The emergence of deep learning has ushered in a transformative era within the realm of image processing, notably in the context of Computed Tomography (CT). Nevertheless, it is noteworthy that a majority of image processing algorithms traditionally rely on processed or reconstructed images, often overlooking the raw sensor data. This thesis, however, shifts its focus toward the utilization of unprocessed computed tomography data, which we refer to as sinogram. Within this framework, we present a comprehensive three-step deep learning algorithm, leveraging a UNet-based architecture, designed to identify and analyze defects within objects without resorting to image reconstruction. The initial phase entails sinogram segmentation, facilitating the extraction of defect masks within the sinogram. Subsequently, instance segmentation is employed to effectively segregate these masks, resulting in their individualization. Lastly, the isolated masks are subjected to thorough defect analysis. Our research endeavors encompass comprehensive experimentation, conducted on both simulated datasets and real-world data.