Browsing by Author "Alam, Shahed"

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A quadruped robot for real-time inspection and responsive operations
(BRAC University, 2026-05) Hossain, Md. Shahariar; Abrar, Rafin Md; Shadid Al Akib; Ahmed, Tazwar; Rahman, Touhidur; Alam, Shahed; Oni, Atib Mohammad
Industrial inspection in hazardous environments such as chemical plants, oil and gas facilities, power systems, and nuclear sites poses significant risks to human workers due to exposure to toxic gases, extreme temperatures, high voltage, and confined spaces. To address these challenges, this project presents the design and implementation of a quadrupedal robot capable of performing real-time inspection and responsive operations with minimal human intervention. The robot integrates a multi-sensor inspection framework for detecting structural and operational abnormalities, with particular focus on identifying surface cracks, microcracks or other early signs of equipment degradation. The system architecture combines a legged mobility platform, onboard computing unit, wireless communication module, and sensor suite including camera, ultrasonic, and IR-based sensing components. These subsystems enable the robot to collect and transmit real-time inspection data to operators for rapid monitoring and decision-making. In addition to routine inspection, the robot is also equipped with a manipulation mechanism for limited emergency operations such as operating switches or mechanical levers when direct human access is not feasible. The quadruped structure provides greater adaptability than conventional wheeled or tracked platforms in stairs, narrow passages, and uneven surfaces, making it more suitable for industrial inspection scenarios. This study addresses key limitations in existing inspection systems, including limited autonomy, infrastructure dependency, and high operational costs, by proposing a modular, cost-effective, and energy-efficient design. Experimental analysis demonstrates that the system can perform reliable inspections while reducing human exposure to hazardous environments. By enabling real-time crack and microcrack detection alongside general hazard inspection, the system supports predictive maintenance, reduces unplanned downtime, and improves worker safety. Overall, this work contributes a modular and practical robotic solution aligned with the growing industrial demand for intelligent, autonomous, and inspection-centric systems.
Design and implementation of a hand gesture-controlled wheelchair
(BRAC University, 2025-01) Shil, Rohit; Islam, Md. Moinul; Mamun, Abdullah Al; Mahamud, Arian; Rahman, Touhidur; Alam, Shahed
The head movement-controlled electric wheelchair is a significant advancement in helping enhance the mobility of disabled persons. This ingenious system involves a cap-mounted accelerometer hooked up to an Arduino Nano microcontroller, which detects movements and maps them to directional instructions. This enables the exact functioning of motors with the help of a dual motor driver which is powered by a 24V battery. To guarantee the safety of the device, a voltage regulation mechanism on the exporting side acts to protect components by holding power at a specific point. Thanks to adaptive code logic, the hands-free device is made smoother and more stable throughout the project. The joystick-free system benefits user function ability but could be a cause for concern for users with a restricted hand motion. A completely new hardwired configuration (and some coding to eliminate signal noise while moving). This outcome is a cost-effective, intuitive solution that significantly enhances independence and quality of life for the end-user. This device effectively solves the theoretical problem of helping the impaired navigate through a combination of planning and design, which is what makes it such a unique solution.
Design, modeling and control of a manipulator with bio-inspired soft robotic gripper
(BRAC University, 2026-01) Hassan, Mehedi; Anik, Tasnim Mahmud; Ahmed, Shafin; Talha, Abu; Rahman, Touhidur; Alam, Shahed; Oni, Atib Mohammad
Safe manipulation of delicate and irregularly shaped objects remains a major challenge for conventional rigid robotic systems due to their limited compliance and adaptability during physical interaction. To address this issue, this project presents a soft robotic manipulation system that integrates a 4-degree-of-freedom (4-DoF) rigid manipulator with a bio-inspired soft gripper, enabling adaptive grasping while supporting both manual and autonomous control modes. The system is developed using a structured, model-based design approach, beginning with theoretical kinematic and dynamic analysis, along with payload torque calculations to guide actuator selection and mechanical configuration. Based on these analyses, the manipulator and gripper are designed and evaluated in Autodesk Fusion 360, including static stress and motion studies, and subsequently fabricated using 3D printing. For modeling and control, a URDF-based robot description is implemented in a ROS2 and MoveIt2 environment, enabling collision-aware motion planning, workspace analysis, and repeatable end-effector positioning in both simulation and hardware. Numerical workspace sampling shows that the manipulator achieves an asymmetric reachable volume of approximately 0.77 m³ under joint and self-collision constraints. Autonomous perception is achieved using a vision-based object detection pipeline, where four deep learning models - YOLOv11-m, Faster R-CNN, RF-DETR, and RTMDet-m, are trained and evaluated on an 11-class manipulation dataset. Among these, YOLOv11-m provides the best overall balance of accuracy and efficiency, achieving 94.7% precision, 94.3% recall, and a [email protected]:0.95 of 0.86. The complete perception, planning, control pipeline is validated through simulation and real-world experiments using a ROS2 distributed architecture. Control performance is evaluated across randomized target poses, in simulation autonomous control achieves a mean positioning accuracy of 98.99% with an average execution time of 4.06s, compared to 70.08% accuracy and 46.72s for manual control. Physical experiments on fragile objects demonstrate grasp success rates of 33% in fully autonomous mode with an average execution time of 23s and 66% positioning accuracy under manual teleoperation with an average execution time of 51s. The results confirm the feasibility of the proposed soft robotic manipulation system while highlighting current hardware and actuation limitations. This project provides a practical foundation for low-cost soft robotic manipulators and offers clear opportunities for future improvement in autonomous performance, making it suitable for applications in industrial automation, agriculture, and service robotics where safe and compliant interaction is required.
Development of a smart system to translate sign language for deaf and mute individuals
(BRAC University, 2025-05) Khan, Raheela Rubaiyat; Billah, Modassir; Rounok, MD. Shahriar Rahman; Hossain, MD. Forhad; Rahman, Touhidur; Alam, Shahed; Oni, Atib Mohammad
Communication is another major barrier that the deaf and the mutes are faced with in mostly a vocal society. More than 13 million individuals in Bangladesh experience some hearing impairments and impediments on speech. They use sign language thus they cannot communicate well most of the time because the one being signed can not understand sign language. The idea of the proposed project is to design a smart wearable device that can be used to translate sign language into speech by hard of hearing persons and actors with impaired voices. The system involves flex sensors, a 6 axis accelerometer-gyro which is mounted into a glove in order to pick up the hand motions and hand postures. The data is processed in the form of sensor data transmitted to a microprocessing unit that has a machine learning ready with such information. In case such a person is a deaf mute, he/she will move his/her hands and our model will identify it and provide us with the corresponding sign (word) by means of a speaker. This solution addresses the shortfalls of the earlier systems since it allows translation in real time regardless of the environmental situation. The solution also improves the accessibility to communication, social inclusion, and safety among the users especially in emergencies and the day-to-day situations.
Impact of machine learning and deep learning on biomedical applications and healthcare industry
(BRAC University Research For Development Club (BURED), 2024-07-03) Kabir, Md Saif; Alam, Shahed
A new era of innovation and change in healthcare and medical diagnostics has started as a result of the advancements in Machine Learning (ML) and Deep Learning (DL). Machine learning and deep learning are not just supplemental tools. They are also the catalysts for a paradigm shift in healthcare industry that promises more efficient and individualized healthcare solutions in the nearby future. The substantial impacts of these cutting-edge algorithms on the biomedical applications have been explored in this research paper. With the help of Artificial Intelligence (AI) and its related field, researchers and healthcare professionals now have access to technologies that can analyze large and complicated datasets, extract insightful knowledge, diagnose medical conditions and make incredibly precise predictions.
Solar tree with tracking facilities for solar power output enhancement
(BRAC University, 2024) Thakur, Saklain Nizam; Intisar, Md. Asir; Chowdhury, Al Farabi; Rahman, Touhidur; Alam, Shahed
This project presents a Solar Tree with Tracking Facilities for Solar Power Output Enhancement, integrating multidisciplinary knowledge to optimize solar energy generation. Two design approaches, light sensor-based and time-based, are explored, with the former chosen as the optimal solution. Modern engineering tools like Raspberry Pi and Arduino are utilized. Expected impacts include a functional prototype, increased solar power output, and contributions to sustainability. Ethical considerations address environmental consciousness, equity, and data privacy. Safety measures encompass hazards, user interface design, and emergency protocols. The project reflects responsible engineering practices and offers an innovative solution for cleaner energy sources.