Hospitals play a crucial role in delivering high-quality patient care, and the healthcare industry is experiencing significant changes driven by the integration of advanced technology. This integration aims to enhance patient care, streamline operations, and minimize errors. Therefore, this study seeks to propose a comprehensive decision-making framework for selecting appropriate technologies, focusing on the medication dispensing process within hospital settings. Through an extensive literature review and expert input, seven critical criteria for technology adoption in hospitals have been identified. Additionally, the study considers the Pick-to-Light system, Carousel Storage System, and Robotic Dispensing System as alternative technologies for integrating medication dispensing processes, with a focus on enhancing labeling, picking, and packing activities. The evaluation involves participants from five tertiary care hospitals in Thailand. The Analytic Hierarchy Process (AHP) is used to assess the relative importance of criteria, followed by the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) for technology selection. The analysis results, including criteria weights and technology selection, are presented separately for each hospital case and overall. The top-three criteria identified as having high priority weights across hospitals are budget and funding support (BF) (0.329), staff technological proficiency levels (TP) (0.147), and leadership support (LS) (0.128). These findings can guide healthcare organizations in making informed decisions during technology implementation, particularly in improving medication dispensing processes. The study identifies the “Pick-to-Light” technology as a suitable solution across all hospital cases, as it can streamline dispensing processes, improve accuracy, and enhance overall operational efficiency. This research, along with the proposed decision-making framework, contributes valuable insights for healthcare institutions seeking to effectively select and integrate technologies, ultimately leading to improved outcomes in hospital medication services.

Nowadays, many industries use robots and cameras in tandem to detect specific objects and perform specific tasks. However, misdetection can occur due to inconsistencies in lighting, background, and environment. In order to address the aforementioned issues, this study proposes using a dual arm six-degree-of-freedom (6-DoF) collaborative robot, ABB YuMi, and red, green, blue-depth (RGB-D) camera with YOLOv5 in a pick-and-place application. In order to prepare the dataset, the images are collected and labeled. The dataset has been trained with the YOLOv5 machine learning algorithm. It has taken on the role of weight for real-time detection. When RGB images from a camera are sent to YOLOv5, data pertaining to the bottle’s position x-y and color are extracted from the depth and color images. The position of the robot is used to control its movement. There are three parts to the experiment. To begin, YOLOv5 is tested with and without trained images. Second, YOLOv5 is tested with real-time camera images. Finally, we assume that YOLOv5 has perfect detection and grasping ability. The results were 95, 90, and 90%.

Hot forging is one of the common manufacturing processes for producing brass workpieces. However forging produces flash which is a thin metal part around the desired part formed with an excessive material. Using robots with vision system to manipulate this workpiece has encountered several challenging issues, e.g. the uncertain shape of flash, color, reflection of brass surface, different lighting condition, and the uncertainty surrounding the position and orientation of the workpiece. In this research, Mask region-based convolutional neural network together with image processing is used to resolve these issues. The depth camera can provide images for visual detection. Machine learning Mask region-based convolutional neural network model was trained with color images and the position of the object is determined by the depth image. A dual arm 7 degree of freedom collaborative robot with proposed grasping strategy is used to grasp the workpiece that can be in inappropriate position and pose. Eventually, experiments were conducted to assess the visual detection process and the grasp planning of the robot.

Door-traversal research has been intensively studied. The challenge of the door traversal problem is derived from the requirement of the mobile manipulator robot to regulate the position of its end-effector while moving along a path. This research presents the door traversal of a mobile manipulator robot by simulating the work environment in the Gazebo program, using the Behavior Trees (BT) architecture to decide on various tasks with complex behaviors to manipulate the open and closed-door conditions. This study developed a program using Robot Operating System (ROS2) as middleware to connect different parts of the program. The results of this research show that classical BT still have limitations in the case of robot positioning errors, resulting in the possibility of BT making incorrect decisions. To overcome these limitations, the robot must be able to learn and comply with the errors. However, experiments have shown many advantages of BT, including flexibility in creating complex behaviors, easy understanding of work steps, decision-making processes, and maintenance capabilities that make it easy to modify robot behavior. As a result, we successfully created robot behaviors using BT to address the door traversal problem, encompassing both door opening and closing scenarios. Furthermore, we identified the limitations of position errors affecting the output of the BT.

Autonomous door traversal for mobile manipulators poses significant challenges owing to environmental uncertainties and positioning errors that can hinder precise control of the end effector. This study presents a novel control framework that combines Quadratic Programming (QP) with Cartesian Compliance Control (CCC) to enhance the performance of mobile manipulators in door-opening tasks. The QP formulation optimizes the control objectives and constraints, allowing for effective coordination between the mobile base and manipulator arm. CCC was employed to adapt to external forces and uncertainties, providing smoother and more precise interactions with the door. The proposed approach was validated through extensive simulations and real-world experiments. In the experiment, the performance of Velocity Control (VC) and CCC were quantitatively compared. CCC significantly reduced the applied force on the door, with a maximum force of 4.4 N compared to 11.9 N for VC. These results demonstrate that CCC offers better adaptability, force control, and trajectory accuracy for dynamic door-opening tasks. Overall, the integrated QP-CCC framework provides superior precision, robustness, and efficiency compared with traditional methods, contributing to advancements in autonomous robotic manipulation in complex environments.

Shortage of workers in manufacturing industry has become one of the critical issues in every industry nowadays. Valves manufacturing process is involved forging and trimming process of brass workpieces which are hazardous and able to harm the workers. The aim of this project is to develop an automatic handling of valves in trimming system for replacing human workers. A 6-DOF articulated robotic arm is integrated with vision system for effectively picking the workpieces from the feeder and placing them to the rotating die for being trimmed. One of the challenging problems is the uncertainties of the shape of the forged workpieces related to flash. The specially designed impactive gripper and machine vision are developed to resolve those uncertainties. The gripper is a parallel jaws gripper designed to grip and hold an object with irregular geometries. For the selected model of valve, the system is able to work at 1,033 pieces hour at 96.67% success rate which is comparable to the capacity of the current human workers.