Explore the vast range of titles available.

This study presents an integrated system for object recognition, six-degrees-of-freedom pose estimation, and dexterous manipulation using a JACO robotic arm with an Intel RealSense D435 camera. This system is designed to automate the manipulation of industrial valves by capturing point clouds (PCs) from multiple perspectives to improve the accuracy of pose estimation. The object recognition module includes scene segmentation, geometric primitives recognition, model recognition, and a color-based clustering and integration approach enhanced by a dynamic cluster merging algorithm. Pose estimation is achieved using the random sample consensus algorithm, which predicts position and orientation. The system was tested within a 60° field of view, which extended in all directions in front of the object. The experimental results show that the system performs reliably within acceptable error thresholds for both position and orientation when the objects are within a ±15° range of the camera’s direct view. However, errors increased with more extreme object orientations and distances, particularly when estimating the orientation of ball valves. A zone-based dexterous manipulation strategy was developed to overcome these challenges, where the system adjusts the camera position for optimal conditions. This approach mitigates larger errors in difficult scenarios, enhancing overall system reliability. The key contributions of this research include a novel method for improving object recognition and pose estimation, a technique for increasing the accuracy of pose estimation, and the development of a robot motion model for dexterous manipulation in industrial settings.

Numerous studies have been conducted to prove the calming and stress-reducing effects on humans of visiting aquatic environments. As a result, many institutions have utilized fish to provide entertainment and treat patients. The most common issue in this approach is controlling the movement of fish to facilitate human interaction. This study proposed an interactive robot, a robotic fish, to alter fish swarm behaviors by performing an effective, unobstructed, yet necessary, defined set of actions to enhance human interaction. The approach incorporated a minimalistic but futuristic physical design of the robotic fish with cameras and infrared (IR) sensors, and developed a fish-detecting and swarm pattern-recognizing algorithm. The fish-detecting algorithm was implemented using background subtraction and moving average algorithms with an accuracy of 78%, while the swarm pattern detection implemented with a Convolutional Neural Network (CNN) resulted in a 77.32% accuracy rate. By effectively controlling the behavior and swimming patterns of fish through the smooth movements of the robotic fish, we evaluated the success through repeated trials. Feedback from a randomly selected unbiased group of subjects revealed that the robotic fish improved human interaction with fish by using the proposed set of maneuvers and behavior.

Large area inspection using a robot is critical in a disastrous situation; especially when humans are inhabiting the catastrophic environment. Unlike natural environments, such environments lack details. Thus, creating 3D maps and identifying objects has became a challenge. This research suggests a 3D Point Cloud Data (PCD) merging algorithm for the less textured environment, aiming World Robot Summit Standard Disaster Robotics Challenge 2021 (WRS). Spider2020, a robotic system designed by the Robot Engineering Laboratory, University of Aizu, was used in this research. Detecting QR codes in a wall and merging PCD, and generating a wall map are the two main tasks in the competition. The Zxing library was used to detect and decode QR codes, and the results were quite accurate. Since the 3D mapping environment has fewer textures, decoded QR code locations are used as the PCD mapping markers. The position of the PCD file was taken from the location given by the robotic arm in Spider2020. The accuracy of merging PCD was improved by including the position of PCD files in the merging algorithm. The robotic system can be used for Large area Inspections in a disastrous situation.

Standard Disaster Robotics Challenge of World Robot Summit (WRS) aims to test the ability of robots that can be used as disaster response robots. Robot Engineering Lab of the University of Aizu is developing a robotic system to address challenges in the WRS. The competition has five stages, and the teleoperation robotic system had to be developed to satisfy the requirements of each challenge. REL uses a disaster-response robot called Giraffe, which has the capability of traveling in hard terrain. Open Robot Technology Middleware uses to integrate all of the subsystems inside the robot. Each subsystem has different tasks that process video data, RGB depth data, Point Cloud Data, sensor data and, feedback data. The Robotic system includes 6 cameras and NDI Software Developer Kit used to transmit and view video streams remotely. The video stream from each camera can be viewed separately, and it gives wider control over the robot for the operator. The teleoperation robotic system was tested during a robot demonstration held in Fukushima Robot Test Field, and results were analyzed according to the WRS 2018 competition guidelines. The results were at an acceptable level.