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
วิทยาลัยนวัตกรรมการผลิตขั้นสูง
The research on improving the strength of solid electrolytes aims to enhance the properties of solid electrolyte materials produced from cement and additives that help develop the cement structure to generate electricity. The main components include sodium chloride (NaCl) and graphite, which contribute to the material’s ability to generate a weak electrical current. The objective is to develop an electricity-generating flooring material. This study involves preparing a mixture of cement, water, sodium chloride (NaCl), and graphite to enhance the material’s electrical conductivity. It is highly anticipated that this research will lead to the development of concrete flooring capable of generating electricity and can be further expanded for future applications.
คณะเทคโนโลยีการเกษตร
Supplementing broilers with different levels of fructooligosaccharides (FOS) under stress conditions, such as higher stocking densities and recycled litter that were not a significant difference in broiler performance, carcass quality and meat quality between the FOS-supplemented groups and the control group (p>0.05). FOS supplementation improved intestinal health by increasing the villus height to crypt depth ratio Lactobacillus populations increased, and Escherichia coli decreased with FOS supplementation. The heterophil-to-lymphocyte ratio was reduced which indicated lower stress.
คณะวิศวกรรมศาสตร์
The production process of the food rancidity indicator label consists of three main steps: 1) preparation of the indicator solution, 2) preparation of the cellulose solution, and 3) formation of the sheet. The indicator solution includes bromothymol blue and methyl red, which act as indicators. The cellulose solution consists of hydroxypropyl methylcellulose, carboxymethyl cellulose, sodium hydroxide, polyethylene glycol 400, and the indicator solution. For the sheet formation, the cellulose solution was mixed with natural latex to increase flexibility and impart hydrophobic properties. After drying, the invention appears as a thin, dark blue label. When exposed to volatile compounds from rancid food, the label changes color from dark blue to green, and then to yellow, corresponding to the increasing amount of volatile compounds from the rancid food.