The integration of intelligent robotic systems into human-centric environments, such as laboratories, hospitals, and educational institutions, has become increasingly important due to the growing demand for accessible and context-aware assistants. However, current solutions often lack scalability—for instance, relying on specialized personnel to repeatedly answer the same questions as administrators for specific departments—and adaptability to dynamic environments that require real-time situational responses. This study introduces a novel framework for an interactive robotic assistant (Beckerle et al. , 2017) designed to assist during laboratory tours and mitigate the challenges posed by limited human resources in providing comprehensive information to visitors. The proposed system operates through multiple modes, including standby mode and recognition mode, to ensure seamless interaction and adaptability in various contexts. In standby mode, the robot signals readiness with a smiling face animation while patrolling predefined paths or conserving energy when stationary. Advanced obstacle detection ensures safe navigation in dynamic environments. Recognition mode activates through gestures or wake words, using advanced computer vision and real-time speech recognition to identify users. Facial recognition further classifies individuals as known or unknown, providing personalized greetings or context-specific guidance to enhance user engagement. The proposed robot and its 3D design are shown in Figure 1. In interactive mode, the system integrates advanced technologies, including advanced speech recognition (ASR Whisper), natural language processing (NLP), and a large language model Ollama 3.2 (LLM Predictor, 2025), to provide a user-friendly, context-aware, and adaptable experience. Motivated by the need to engage students and promote interest in the RAI department, which receives over 1,000 visitors annually, it addresses accessibility gaps where human staff may be unavailable. With wake word detection, face and gesture recognition, and LiDAR-based obstacle detection, the robot ensures seamless communication in English, alongside safe and efficient navigation. The Retrieval-Augmented Generation (RAG) human interaction system communicates with the mobile robot, built on ROS1 Noetic, using the MQTT protocol over Ethernet. It publishes navigation goals to the move_base module in ROS, which autonomously handles navigation and obstacle avoidance. A diagram is explained in Figure 2. The framework includes a robust back-end architecture utilizing a combination of MongoDB for information storage and retrieval and a RAG mechanism (Thüs et al., 2024) to process program curriculum information in the form of PDFs. This ensures that the robot provides accurate and contextually relevant answers to user queries. Furthermore, the inclusion of smiling face animations and text-to-speech (TTS BotNoi) enhanced user engagement metrics were derived through a combination of observational studies and surveys, which highlighted significant improvements in user satisfaction and accessibility. This paper also discusses capability to operate in dynamic environments and human-centric spaces. For example, handling interruptions while navigating during a mission. The modular design allows for easy integration of additional features, such as gesture recognition and hardware upgrades, ensuring long-term scalability. However, limitations such as the need for high initial setup costs and dependency on specific hardware configurations are acknowledged. Future work will focus on enhancing the system’s adaptability to diverse languages, expanding its use cases, and exploring collaborative interactions between multiple robots. In conclusion, the proposed interactive robotic assistant represents a significant step forward in bridging the gap between human needs and technological advancements. By combining cutting-edge AI technologies with practical hardware solutions, this work offers a scalable, efficient, and user-friendly system that enhances accessibility and user engagement in human-centric spaces.
งานวิจัยนี้มีที่มาจาก ความต้องการที่เพิ่มขึ้นสำหรับผู้ช่วยอัจฉริยะ ใน สภาพแวดล้อมที่เน้นมนุษย์เป็นศูนย์กลาง เช่น ห้องปฏิบัติการและสถาบันการศึกษา ซึ่งเผชิญปัญหาเรื่อง ข้อจำกัดด้านทรัพยากรบุคคล ในการให้ข้อมูลแก่ผู้เยี่ยมชมและนักศึกษา ปัจจุบัน โซลูชันที่มีอยู่มัก ขาดความสามารถในการขยายขนาด และ ปรับตัวให้เข้ากับสภาพแวดล้อมที่เปลี่ยนแปลง ได้อย่างมีประสิทธิภาพ นอกจากนี้ ระบบผู้ช่วยแบบเดิมมักพึ่งพาบุคลากรเฉพาะทาง ทำให้เกิดภาระในการตอบคำถามซ้ำๆ และไม่สามารถรองรับจำนวนผู้ใช้ที่เพิ่มขึ้นได้ ดังนั้น งานวิจัยนี้จึงมุ่งพัฒนา ผู้ช่วยหุ่นยนต์เชิงโต้ตอบ ที่สามารถ ทำงานอัตโนมัติในสภาพแวดล้อมแบบไดนามิก โดยใช้ AI และโมเดลภาษาขนาดใหญ่ (LLM Predictor) ผสานกับ การรู้จำเสียง ท่าทาง และใบหน้า เพื่อเพิ่ม การมีส่วนร่วมของผู้ใช้ และ ความสามารถในการโต้ตอบ แบบเรียลไทม์ ระบบนี้ยังช่วยลดภาระของบุคลากรและเพิ่ม การเข้าถึงข้อมูล ได้อย่างแม่นยำและมีประสิทธิภาพ อีกทั้งยังรองรับการพัฒนาเพิ่มเติมเพื่อให้สามารถขยายขีดความสามารถและใช้งานได้หลากหลายขึ้นในอนาคต
คณะวิศวกรรมศาสตร์
Jaundice, a common condition in infants that results from high bilirubin levels in the blood, often requires early diagnosis and monitoring to prevent severe complications, especially in newborns. Traditional diagnostic methods can be time-consuming and subject to human error. This study proposes an approach for real-time jaundice detection using advanced image processing techniques and machine learning algorithms. By analyzing images captured in RGB color spaces, pixel values are extracted and processed through Otsu’s thresholding and morphological operations to detect color patterns indicative of jaundice. A classifier model is then trained to distinguish between normal and jaundiced conditions, offering an automated, accurate, and efficient diagnostic tool. The system’s potential to operate in real-time makes it particularly suited for clinical settings, providing healthcare professionals with timely insights to improve patient outcomes. The proposed method represents a significant innovation in healthcare, combining artificial intelligence and medical imaging to enhance the early detection and management of jaundice, reducing reliance on manual interventions and improving overall healthcare delivery.
คณะสถาปัตยกรรม ศิลปะและการออกแบบ
A Photographic series that expresses the abstract states of myself, towards the question of existence that results from being surrounded by expectations of both surrender and freedom of expression, this series focuses on my own subjectivities in order to bring back memories of almost forgotten feelings and make them clear once more.
คณะวิศวกรรมศาสตร์
SecurionSphere is the penetration testing learning platform that focuses on web application exploitation. This platform is intended to address concerns seen in existing penetration testing platforms, such as resource sharing that may affect other users and the constant environment configuration the permits the same response leading to copy the answer from others. Supervisors can use templates to address various forms of web application vulnerability threats. Users can generate the instance of supervisor's templates machine. The platform also randomly generates the environment configuration for each machine has the difference environment and the answer. This allows the users get more realistic learning experiences without affecting the resources of others.