Robotic picking and placing played an essential role in Industrial 4.0 and have long been recognized as significant contributions to industrial processes. Various scenarios involve picking and placing parts for assembly in industrial production, such as assembling different electronic components in the manufacturing process. Those tasks require high precision to complete. However, achieving high precision in the assembly of CPUs poses a significant challenge, particularly when dealing with reflective surfaces. This paper presents a strategic system design tailored to address these challenges effectively. We focus on system device choice and optimizing the key parameters of the sensor system to strike a balance between device cost and the required precision. We use methods to construct the whole robot manipulation system, such as geometric segmentation, binocular vision with structure light projection and, based on 3D information, 6D pose estimation to construct the system. The results of our study demonstrate the practical applicability and benefits of this strategic system design in industrial settings. By meeting strict system accuracy requirements, our approach contributes to advancing industry practices and growing its impact on society.

Chinese calligraphy is a complicated art form that requires precise control of movement and brush dynamics, making it a challenging task for robotic systems. This paper presents a novel approach to enabling robotic manipulators to perform Chinese calligraphy using 3D path planning algorithms and a large language model (LLM) as input generators. The proposed framework integrates an LLM to generate textual content prompts, which are then transformed into detailed 3D Bézier curve trajectories which represent the strokes of Chinese characters. These trajectories are optimized to ensure smooth and accurate motion of the robotic manipulator, taking into account the brush's pressure, orientation, and speed to emulate traditional calligraphy techniques. Experimental results demonstrate the effectiveness of the approach in producing visually authentic Chinese calligraphy with high precision and artistic quality. This work highlights the potential of combining LLM with advanced robotic path planning to bridge the gap between traditional art and robot world.

This paper explores the potential of underwater robotics competitions as a platform for promoting integrated STEM education, utilizing project-based learning, engineering design processes, and Bloom's Taxonomy. The study examines how students can develop essential STEM skills and knowledge by participating in such competitions, which require the design, construction, and operation of underwater vehicles to tackle real-world challenges. By engaging in engineering design processes, students learn to identify problems, brainstorm solutions, prototype, test, and iterate their designs. Project-based learning allows students to work collaboratively, applying interdisciplinary knowledge and skills to achieve a common goal. Furthermore, the paper discusses how Bloom's Taxonomy can be used to assess the cognitive development of participants, ensuring that the competition activities cater to various levels of learning, from basic knowledge acquisition to complex problem-solving and critical thinking. The findings of this study highlight the potential of underwater robotics competitions as an effective and engaging approach to delivering integrated STEM education, preparing students for future careers in science, technology, engineering, and mathematics.

This paper explores the potential of virtual underwater robotics experiences to promote inclusive education and enhance STEM learning and collaboration with real-world applications. The study highlights how students from diverse backgrounds can develop essential STEM skills and knowledge by participating in virtual activities that require the design, construction, and operation of underwater vehicles to address real-world challenges. By engaging in collaborative learning, students learn to apply interdisciplinary knowledge and skills to achieve a common goal, while also developing social and emotional skills such as communication, empathy, and respect for others. The paper also discusses how the virtual experience can be used to promote real-world applications in STEM education, preparing students for future careers in science, technology, engineering, and mathematics. The study identifies the challenges and lessons learned from implementing virtual experiences and provides recommendations for future implementation. The findings suggest that virtual underwater robotics experiences can be an effective and inclusive approach to delivering integrated STEM education, providing a unique opportunity for students to develop problem-solving, critical thinking, collaboration, and teamwork skills while exploring the exciting world of underwater robotics. By promoting inclusive education through virtual underwater robotics experiences, students from diverse backgrounds can have equal access to STEM learning and collaboration, leading to greater opportunities for success in their future careers.

This paper constructs a simulated CPU assembly line robot system. The system incorporates binocular structured light vision technology for CPU recognition and localization. The reflective surface of the CPU poses a significant challenge for 3D reconstruction, mainly when dealing with tilted angle situations. This inherent characteristic of the CPU adds complexity to the vision system to capture and reconstruct the object's 3D information accurately. This research aims to optimize the vision system's key parameters, balancing cost-effectiveness and the required precision. By achieving the necessary level of accuracy in picking the CPUs to the desired location, our work aims to enhance robotic assembly operations, offering an optimized parameter configuration approach and supporting the needs of modern industrial processes.

This paper considers the question of measuring attitude using magnetometer-equipped Inertial Measurement Units (IMUs) when in the presence of magnetic disturbance. Specifically, for use in micro-observational class underwater vehicles. Such vehicles' small size and mobility suit activities in coral reefs but often face significant magnetic interference due to high metal debris content, especially in artificial reefs. Their small size also means motor thrusters are situated closer to the magnetometers, producing additional interference. An Adaptive Indirect Kalman Filter is proposed that uses IMU and motor speed measurements to deal with two main sources of disturbances: magnetic fields from foreign objects, and from the vehicle's own motors rotating in slow speed. The method has been evaluated using a custom underwater vehicle made in HKUST, and results show that the proposed method is able to obtain accurate orientation representations even when approaching objects with strong magnetic fields or when the motors are producing interference by rotating in slow speeds.

Location and tracking of healthy or infirm elders is a potentially useful application as society aging is accelerating worldwide and as technologies such as cellular positioning and Assisted Global Positioning System (AGPS) are maturing. For a wearable device to be used in such an application, the battery operating hour is often a key consideration. This paper describes an energy-efficient cellular and AGPS tracking algorithm based on the concept of Personal Common Location (PCL) that we define as the locations at which an elder spends most of his/her time, as recognized from historic cell ID record. During the tracking process, AGPS fixes are avoided if the elderly carrier is determined to be located within a PCL, if the destination PCL can be predicted, or if AGPS signals are likely to be unavailable. This paper also describes our approach for off-line PCL recognition, the necessity of cell ID clustering, as well as the results from our tracking experiments.

In the use of a wearable GPS and cellular tracker for applications such as elderly tracking, device power consumption is an important consideration. To save power, assisted GPS (AGPS) location fixes should not be performed frequently. On the other hand, we also do not want to lose important information about the user's mobility patterns and routines. To solve this dilemma, in this paper, we present the design of a system that intelligently schedules on-line AGPS location fixes only when necessary based on information extracted from user's historical mobility data, and then reconstruct the user path based on these sparsely taken on-line location fixes. Experimental results show that our on-line algorithm can significantly reduce the number of AGPS fixes needed and the reconstruction method works well without a priori knowledge of a map and streets information.

This paper explores the use of terrestrial time of arrival (TOA) measurements to improve the initial Global Positioning System (GPS) location fix accuracy. First, we present a geometric approach when a GPS location fix and one TOA measurement are available. Then, a more general hybrid GPS/TOA method via the Weighted Least Square Estimator (WLSE) is proposed. To simplify the calculation, a closed-form solution based on the two-step Least Square approach is also designed. The Cramer-Rao Lower Bound (CRLB) is derived as a performance benchmark. Simulation results exhibit excellent performance of the proposed methods which attain the CRLB in different scenarios. The proposed methods work even if only one TOA measurement (in addition to a GPS location fix) is available and the corresponding accuracy improvement (compared with the initial GPS location fix) can be as much as 30%.

This paper proposes a hybrid TOA/AOA (Time of Arrival/Angle of Arrival)-based localization algorithm for Code Division Multiple Access (CDMA) networks. The algorithm extends the Taylor Series Least Square (TS-LS) method originally developed for TOA-based systems to incorporate AOA measurements. In addition, tracking algorithms utilizing velocity and acceleration measurements are investigated. Simulation results illustrate that the proposed TOA/AOA TS-LS can provide better performance than conventional schemes in localization accuracy and in reduced likelihood of encountering non-convergence problem compared with TOA TS-LS. Tracking algorithms using the Extended and Unscented Kalman Filter (EKF and UKF) can track the objects relatively well, further decreasing the positioning error. UKF is found to provide closer tracking of the trajectory than EKF, for it truly captures the statistical mean and variance of the noises.