#educationstudies

RGR Siddhanthi B.Ed College is a reputed teacher-education institution in Hyderabad, dedicated to preparing skilled, ethical, and confident educators for tomorrow’s classrooms. With a strong academic foundation and a student-centric approach, the college plays a vital role in nurturing future teachers who can inspire and lead.

About the College

RGR Siddhanthi B.Ed College operates under the R.G.R. Siddhanthi Educational Society, which has a long-standing commitment to quality education. The college offers the Bachelor of Education (B.Ed) program, designed in line with university and regulatory guidelines, focusing on both theoretical understanding and practical teaching skills.

The institution emphasizes:

Strong pedagogical training

Value-based and inclusive education

Professional ethics in teaching

Holistic personality development

Academic Excellence & Training

The B.Ed program at RGR Siddhanthi College combines classroom learning with hands-on teaching practice. Students receive training in:

Modern teaching methodologies

Classroom management skills

Educational psychology

Lesson planning and assessment techniques

Teaching practice through school internships

Experienced faculty members mentor students throughout the course, ensuring they are well prepared for real-world teaching environments.

Why Choose RGR Siddhanthi B.Ed College?

🎓 Dedicated Teacher-Training Focus

👩‍🏫 Experienced & Supportive Faculty

🏫 Well-structured Curriculum aligned with current education standards

📘 Practical Teaching Exposure through internships and practice teaching

🌱 Holistic Development with co-curricular and academic activities

The college aims to empower aspiring teachers with knowledge, confidence, and a passion for lifelong learning.

Career Opportunities

Graduates of the B.Ed program can pursue careers as:

School teachers (primary & secondary)

Education counselors

Academic coordinators

Tutors and trainers

AI-driven drone from University of Klagenfurt uses IDS uEye camera for real-time, object-relative navigation—enabling safer, more efficient, and precise inspections. High-voltage power lines. Electricity distribution station. high voltage electric transmission tower. Distribution electric substation with power lines and transformers. The inspection of critical infrastructures such as energy plants, bridges or industrial complexes is essential to ensure their safety, reliability and long-term functionality. Traditional inspection methods always require the use of people in areas that are difficult to access or risky. Autonomous mobile robots offer great potential for making inspections more efficient, safer and more accurate. Uncrewed aerial vehicles (UAVs) such as drones in particular have become established as promising platforms, as they can be used flexibly and can even reach areas that are difficult to access from the air. One of the biggest challenges here is to navigate the drone precisely relative to the objects to be inspected in order to reliably capture high-resolution image data or other sensor data. A research group at the University of Klagenfurt has designed a real-time capable drone based on object-relative navigation using artificial intelligence. Also on board: a USB3 Vision industrial camera from the uEye LE family from IDS Imaging Development Systems GmbH. As part of the research project, which was funded by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the drone must autonomously recognise what is a power pole and what is an insulator on the power pole. It will fly around the insulator at a distance of three meters and take pictures. "Precise localisation is important such that the camera recordings can also be compared across multiple inspection flights," explains Thomas Georg Jantos, PhD student and member of the Control of Networked Systems research group at the University of Klagenfurt. The prerequisite for this is that object-relative navigation must be able to extract so-called semantic information about the objects in question from the raw sensory data captured by the camera. Semantic information makes raw data, in this case the camera images, "understandable" and makes it possible not only to capture the environment, but also to correctly identify and localise relevant objects. In this case, this means that an image pixel is not only understood as an independent colour value (e.g. RGB value), but as part of an object, e.g. an isolator. In contrast to classic GNNS (Global Navigation Satellite System), this approach not only provides a position in space, but also a precise relative position and orientation with respect to the object to be inspected (e.g. "Drone is located 1.5m to the left of the upper insulator"). The key requirement is that image processing and data interpretation must be latency-free so that the drone can adapt its navigation and interaction to the specific conditions and requirements of the inspection task in real time. Thomas Jantos with the inspection drone - Photo: aau/Müller Semantic information through intelligent image processing Object recognition, object classification and object pose estimation are performed using artificial intelligence in image processing. "In contrast to GNSS-based inspection approaches using drones, our AI with its semantic information enables the inspection of the infrastructure to be inspected from certain reproducible viewpoints," explains Thomas Jantos. "In addition, the chosen approach does not suffer from the usual GNSS problems such as multi-pathing and shadowing caused by large infrastructures or valleys, which can lead to signal degradation and thus to safety risks." A USB3 uEye LE serves as the quadcopter's navigation camera How much AI fits into a small quadcopter? The hardware setup consists of a TWINs Science Copter platform equipped with a Pixhawk

PX4 autopilot, an NVIDIA Jetson Orin AGX 64GB DevKit as on-board computer and a USB3 Vision industrial camera from IDS. "The challenge is to get the artificial intelligence onto the small helicopters. The computers on the drone are still too slow compared to the computers used to train the AI. With the first successful tests, this is still the subject of current research," says Thomas Jantos, describing the problem of further optimising the high-performance AI model for use on the on-board computer. The camera, on the other hand, delivers perfect basic data straight away, as the tests in the university's own drone hall show. When selecting a suitable camera model, it was not just a question of meeting the requirements in terms of speed, size, protection class and, last but not least, price. "The camera's capabilities are essential for the inspection system's innovative AI-based navigation algorithm," says Thomas Jantos. He opted for the U3-3276LE C-HQ model, a space-saving and cost-effective project camera from the uEye LE family. The integrated Sony Pregius IMX265 sensor is probably the best CMOS image sensor in the 3 MP class and enables a resolution of 3.19 megapixels (2064 x 1544 px) with a frame rate of up to 58.0 fps. The integrated 1/1.8" global shutter, which does not produce any 'distorted' images at these short exposure times compared to a rolling shutter, is decisive for the performance of the sensor. "To ensure a safe and robust inspection flight, high image quality and frame rates are essential," Thomas Jantos emphasises. As a navigation camera, the uEye LE provides the embedded AI with the comprehensive image data that the on-board computer needs to calculate the relative position and orientation with respect to the object to be inspected. Based on this information, the drone is able to correct its pose in real time. The IDS camera is connected to the on-board computer via a USB3 interface. "With the help of the IDS peak SDK, we can integrate the camera and its functionalities very easily into the ROS (Robot Operating System) and thus into our drone," explains Thomas Jantos. IDS peak also enables efficient raw image processing and simple adjustment of recording parameters such as auto exposure, auto white Balancing, auto gain and image downsampling. To ensure a high level of autonomy, control, mission management, safety monitoring and data recording, the researchers use the source-available CNS Flight Stack on the on-board computer. The CNS Flight Stack includes software modules for navigation, sensor fusion and control algorithms and enables the autonomous execution of reproducible and customisable missions. "The modularity of the CNS Flight Stack and the ROS interfaces enable us to seamlessly integrate our sensors and the AI-based 'state estimator' for position detection into the entire stack and thus realise autonomous UAV flights. The functionality of our approach is being analysed and developed using the example of an inspection flight around a power pole in the drone hall at the University of Klagenfurt," explains Thomas Jantos. Visualisation of the flight path of an inspection flight around an electricity pole model with three insulators in the research laboratory at the University of Klagenfurt Precise, autonomous alignment through sensor fusion The high-frequency control signals for the drone are generated by the IMU (Inertial Measurement Unit). Sensor fusion with camera data, LIDAR or GNSS (Global Navigation Satellite System) enables real-time navigation and stabilisation of the drone - for example for position corrections or precise alignment with inspection objects. For the Klagenfurt drone, the IMU of the PX4 is used as a dynamic model in an EKF (Extended Kalman Filter). The EKF estimates where the drone should be now based on the last known position, speed and attitude. New data (e.g. from IMU, GNSS or camera) is then recorded at up to 200 Hz and incorprated into the state estimation process.

The HP Robots Otto is a versatile, modular robot designed specifically for educational purposes. It offers students and teachers an exciting opportunity to immerse themselves in the world of robotics, 3D printing, electronics and programming. The robot was developed by HP as part of their robotics initiative and is particularly suitable for use in science, technology, engineering and mathematics (STEM) classes. https://www.youtube.com/watch?v=ok1GaEeTGPA Key features of Otto: Modular design: Otto is a modular robot that allows students to build, program and customize it through extensions. This promotes an understanding of technology and creativity. The modular structure allows various components such as motors, sensors and LEDs to be added or replaced, which increases the learning curve for students. Programmability: The robot can be programmed with various programming languages, including block-based programming for beginners and Python and C++ for advanced programmers. This diversity allows students to continuously improve their coding skills and adapt to the complexity of the tasks. Sensors and functions: Equipped with ultrasonic sensors for obstacle detection, line tracking sensors and RGB LEDs, Otto offers numerous interactive possibilities. These features allow students to program complex tasks such as navigating courses or tracing lines. The sensors help to detect the environment and react accordingly. 3D printing and customizability: Students can design Otto's outer parts themselves and produce them with a 3D printer. This allows for further personalization and customization of the robot. This creative freedom not only promotes technical understanding, but also artistic skills. Own parts can be designed and sensors can be attached to desired locations. Educational approach: Otto is ideal for use in schools and is aimed at students from the age of 8. Younger students can work under supervision, while older students from the age of 14 can also use and expand the robot independently. The kit contains all the necessary components to build a functioning robot, including motors, sensors, and a rechargeable battery. Programming environments: Otto is programmed via a web-based platform that runs on all operating systems. This platform offers different modes: Block-based programming: Similar to Scratch Jr., ideal for beginners. This visual programming makes it easier to get started in the world of programming and helps students understand basic concepts such as loops and conditions. Python: A Python editor is available for advanced users. Python is a popular language that works well for teaching because it is easy to read and write. Students can use Python to develop more complex algorithms and expand their programming skills. C++: Compatible with the Arduino IDE for users who have deeper programming knowledge. C++ offers a high degree of flexibility and allows students to access the hardware directly, allowing for their own advanced projects. https://www.youtube.com/watch?v=v5Otdd4fogs&t=3s Expansion Kits: In addition to the Starter Kit, there are several expansion kits. All expansion kits require the starter kit, as they are built on top of it. Emote Expansion Kit: It includes components such as an LED matrix display, OLED display, and an MP3 player that allow the robot to display visual and acoustic responses. This kit is particularly suitable for creative projects where Otto should act as an interactive companion. The emote kit allows Otto to show emotions, mirror human interactions, and develop different personalities. Sense Expansion Kit: With the Sense Kit, Otto can perceive its surroundings through various sensors. Included are sensors for temperature, humidity, light and noise as well as an inclination sensor. These enable a wide range of interactions with the environment. The kit is ideal for projects that focus on environmental detection and data analysis.

Der HP Robots Otto ist ein vielseitiger, modularer Roboter, der speziell für Bildungszwecke entwickelt wurde. Er bietet Schülern und Lehrern eine spannende Möglichkeit, in die Welt der Robotik, 3D-Druck, Elektronik und Programmierung einzutauchen. Der Roboter wurde von HP als Teil ihrer Robotik-Initiative entwickelt und ist besonders für den Einsatz im MINT-Unterricht (Mathematik, Informatik, Naturwissenschaften und Technik) geeignet. https://www.youtube.com/watch?v=ok1GaEeTGPA Hauptmerkmale von Otto: Modularer Aufbau: Otto ist ein modularer Roboter, der es Schülern ermöglicht, ihn zu bauen, zu programmieren und durch Erweiterungen individuell anzupassen. Dies fördert das Verständnis für Technik und Kreativität. Die modulare Struktur erlaubt es, verschiedene Komponenten wie Motoren, Sensoren und LEDs hinzuzufügen oder zu ersetzen, was die Lernkurve für Schüler erweitert. Programmierbarkeit: Der Roboter kann mit verschiedenen Programmiersprachen programmiert werden, darunter blockbasierte Programmierung für Anfänger sowie Python und C++ für Fortgeschrittene. Diese Vielfalt ermöglicht es Schülern, ihre Programmierfähigkeiten kontinuierlich zu verbessern und sich an die Komplexität der Aufgaben anzupassen. Sensoren und Funktionen: Ausgestattet mit Ultraschallsensoren zur Hinderniserkennung, Linienverfolgungssensoren und RGB-LEDs bietet Otto zahlreiche interaktive Möglichkeiten. Diese Funktionen ermöglichen es Schülern, komplexe Aufgaben wie das Navigieren durch Parcours oder das Verfolgen von Linien zu programmieren. Die Sensoren helfen dabei, die Umgebung zu erkennen und entsprechend zu reagieren. 3D-Druck und Anpassbarkeit: Schüler können Ottos äußere Teile selbst entwerfen und mit einem 3D-Drucker herstellen. Dies ermöglicht eine weitere Personalisierung und Anpassung des Roboters. Diese Kreativfreiheit fördert nicht nur technisches Verständnis, sondern auch künstlerische Fähigkeiten. Eigene Teile können entworfen und Sensoren an gewünschten Stellen angebracht werden. Bildungsansatz: Otto ist ideal für den Einsatz in Schulen gedacht und richtet sich an Schüler ab 8 Jahren. Jüngere Schüler können unter Aufsicht arbeiten, während ältere Schüler ab 14 Jahren den Roboter auch eigenständig nutzen und erweitern können. Das Kit enthält alle notwendigen Komponenten, um einen funktionierenden Roboter zu bauen, einschließlich Motoren, Sensoren und einer wiederaufladbaren Batterie. Programmierumgebungen: Die Programmierung von Otto erfolgt über eine webbasierte Plattform, die auf allen Betriebssystemen läuft. Diese Plattform bietet verschiedene Modi: Blockbasierte Programmierung: Ähnlich wie Scratch Jr., ideal für Anfänger. Diese visuelle Programmierung erleichtert den Einstieg in die Welt der Programmierung und hilft Schülern, grundlegende Konzepte wie Schleifen und Bedingungen zu verstehen. Python: Für fortgeschrittene Benutzer steht ein Python-Editor zur Verfügung. Python ist eine beliebte Sprache, die sich gut für den Unterricht eignet, da sie einfach zu lesen und zu schreiben ist. Schüler können mit Python komplexere Algorithmen entwickeln und ihre Fähigkeiten im Bereich der Programmierung erweitern. C++: Kompatibel mit der Arduino IDE für Nutzer, die tiefere Programmierkenntnisse haben. C++ bietet eine hohe Flexibilität und ermöglicht es Schülern, direkt auf die Hardware zuzugreifen, was eigene fortgeschrittene Projekte ermöglicht. https://www.youtube.com/watch?v=v5Otdd4fogs&t=3s Expansion/Erweiterungs Kits: Zusätzlich zum Starter Kit gibt es mehrere Erweiterungskits. Alle Erweiterungskits setzen das Starter-Kit voraus, da sie auf dessen Basis aufgebaut werden. Emote Expansion Kit: Es enthält Komponenten wie ein LED-Matrix-Display, OLED Display und einen MP3-Player, die es dem Roboter ermöglichen, visuelle und akustische Reaktionen darzustellen. Dieses Kit eignet sich besonders für kreative Projekte, bei denen Otto als interaktiver Begleiter fungieren soll.