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
คณะอุตสาหกรรมอาหาร
Spent coffee grounds (SCGs) are waste from coffee drink process, which are rich of a varieties of nutrients. This research applied SCGs as ingredient in cracker. The optimized formula and process are studied as well as addition of different levels of SCGs were studied. It was found that addition of SCGs in cracker had hedonic score in high level from panels, especially panels who drink coffee. Moreover, it was observed that SCGs could increase nutrients especially carbohydrate and fiber to the product.
วิทยาลัยนวัตกรรมการผลิตขั้นสูง
This research presents a Digital Twin of an Aquarium for Water Quality Monitoring, developing a virtual model that displays real-time key water parameters, including pH level, temperature, flow rate, and dissolved oxygen. Sensor data is processed and visualized through a Graphical User Interface (GUI) to reflect the real-time status of the virtual aquarium. This system enables accurate water quality monitoring and analysis while reducing reliance on expensive software solutions.
คณะวิทยาศาสตร์
This study aimed to investigate the effectiveness of extracts from moringa seeds, roselle seeds, and tamarind seeds as coagulants to improve water quality in surface water sources. Extracts from these seeds serve as environmentally friendly coagulants and provide alternative options for enhancing surface water quality. The turbidity of surface water sources ranged between 14 and 24 NTU. The coagulation process used the Jar Test method, where the moringa seed, roselle seed, and tamarind seed extracts functioned as both primary coagulants and coagulant aids. In the preparation process, the seeds were finely ground and extracted using a 0.5-M sodium chloride (NaCl) solution. These extracts were then applied as coagulants to reduce turbidity and enhance water quality, with each concentration tested in 300 ml of water. The results indicated that the most effective way to remove turbidity using 2,000 mg/L of moringa seed extract, achieving a turbidity reduction of approximately 73.19% at a cost of 0.0309 baht per 300 ml of water. Followed by Tamarind seed extract, with a concentration of 4,000 mg/L, followed with a turbidity reduction of approximately 56.75% at a cost of 0.0933 baht per 300 ml. Lastly, roselle seed extract at 6,000 mg/L achieved a turbidity reduction of approximately 32.67% at a cost of 0.0567 baht per 300 ml of water.