Prototypes & research projects

PROTOTYPES

Features:  

3D Hand gesture based operation 

Battery Operated 

Can perform aggressive maneuvers. 

Promising Autonomous flight technology 

Click on Picture to see all Video Demonstrations

Features:  

Data logging using SD Card 

Remotely Operated Using RC Control (6-Channels)

Battery Operated 

Can lift up the payload upto 500 grams using Robotic Manipulator 


Click on Picture to see all Video Demonstrations

Features:  

Data logging using SD Card 

Remotely Operated Using Tello Drone Application

Reduced the chattering noise and can lift 50 grams of weight

Having mini-solenoid gripper to drop the payload


Click on Picture to see all Video Demonstrations

Features:  

Data logging using SD Card 

Remotely Operated Using RC Control (6-Channels)

Battery Operated 

Can lift up the payload upto 50 grams 

Shares the parameters using IoT Broker 


Underactuated Aircushion Vehicle on Carbon Fibre Structure  

Features:  

Data and Video logging using SD Card 

Wired-Operated 

Shares the parameters using IoT Broker

Identify some of the sea-creatures 


Unmanned Under-water Vehicle (UUV)

Features:  

IoT Enabled

Monitors temperature, altitude and environmental pressure

Identifies the issues in sewerage line based on computer vision techniques 

Clears the blockage using special rudder design operated by BLDC motor

Hazardous Gas Detection using MQ sensors 


Sewerbot Design with Rudder Design

Features:  

IoT Enabled

Monitors temperature, altitude and environmental pressure

Localization and Mapping feature 


Unmanned Ground Vehicle with SLAM

Features:  

IoT Enabled

Pick and drop the package as per color detections

Different Color Detection

IoT and Computer Vision enabled Robotic Manipulator Design


Features:  

IoT Enabled

Tracks sun and produces maximum power readings

Shares data on IoT Broker "Connect Things"

IoT enabled Solar Tracking and Monitoring


Features:  

Wheat growth detection as per color and height 

Temperature and humidity sensors 

Water Sprinkling mechanism as per temperature 

Computer Vision based Crop Monitoring


Features:  

Input current and voltage measurements

Output current and voltage measurements 

Load management and switching 

LabVIEW based Graphical User Interface (GUI) 

Load Management System Using LabVIEW


Research Projects

Principal Investigator: Dr. Saiful Azrin B M Zulkifli

Main Research officer: Muhammad Irsyad Sahalan 

Co-Researcher: Ghulam E Mustafa Abro

Grant Name: International Collaborative Research Fund RG2021-0873

Partners

Universitas Muhammadiyah Surkarta Indonesia and Universiti Teknologi PETRONAS, Malaysia

Abstract: 

This project aims to deliver an indoor logistics delivery drone without the use of global navigation satellite systems (GNSS). This requires an alternative approach to achieve accurate state estimation. Therefore vision-based state estimation is proposed for indoor navigation. Camera carries the advantage of being low cost, low power and reliable provided armed with advanced algorithms. This vision approach targets to utilize fiducial markers with monocular camera. When a marker is captured by a camera, its position and orientation with respect to the camera’s coordinate frame is determined based on its homography transformation. The pose of the camera, and hence the drone, in world coordinate can then be inferred from known markers poses on the provided map. The advantage of using such markers is that it can be easily detected using standard pattern recognition algorithms and conventional classification methods. The development of fiducial marker localization can be applied to various scenarios such as non human contact robot in hospital, load carriage in warehouse, surveilance in complexes, and etc.

Principal Investigator: Dr. Saiful Azrin B M Zulkifli

Team Members

Mohamad Mirza Sadiq, Ghulam E Mustafa Abro, Ahmad Zaid Syakir Mohd Yazsid

Independent Project 

Abstract: 

One of the biggest obstacles to the adoption of electric vehicles is their limited operating range per full charge of the onboard energy storage. This issue is known as range anxiety - people are reluctant to depend on EVs due to myriad reasons such as running out of stored energy, lack of charging facilities, and the time it takes to charge. Much research has been carried out to address this concern; some propose combining conventional and electric engines for the best of both worlds, others propose strategically placing charging stations, and still others propose using algorithms to determine the best route for the vehicle. This research attempts to implement a simple current limiting mechanism for existing personal electric mobility (PEM) equipment such as electric scooters, which will track its onboard battery state-of-charge (SoC) and produce a new signal that controls the level of current provided to the motor. This can help to limit energy depletion from the battery and thus extend the scooter’s range. By using MATLAB-Simulink software, this study simulates a PEM and the current limiting mechanism. With the use of Arduino circuitry, this research involves tapping into the existing electric scooter to record and analyze serial communication between the user interface and motor controller, to limit the motor’s current and achieve longer range. 

Principal Investigator: Ghulam E Mustafa Abro 

Grant Name: IEEE HAC & SIGHT Grant No: 22COVID20

Team Members

Khairel Danish Khairil Anwar, Dr Saiful Azrin B M Zulkifli, Dr. Vijanth Sagayan Asirvadam, 

Abstract

Without a doubt, autonomous unmanned aerial vehicles (UAVs) will increase quickly and be used more and more in the coming decades. These unmanned aerial vehicles, also known as drones, are either designed to help people or perform tasks that involve people. These drones have grown to be faster, easier, and less expensive by integrating several technologies and supported by hybrid algorithms. These machines can also do a variety of tedious, filthy, challenging, and hazardous tasks. The deployment of machine learning- or artificial intelligence-based algorithms that can give drones today an autonomous flight mode and computer vision capabilities can be seen in the area of combining various technologies and hybrid algorithms. In order to stop the spread of COVID-19 among the crowd, this topic has been studied. It shares the prototyping and development of an autonomous quadrotor UAV based on machine learning and has a potential to maneuver and detect object. In order to identify the hand motion and attempt to keep a certain distance from the human, our proposed algorithm would stimulate the drone. Spraying disinfectants on the user's hands is the main idea. This research is one of the ways to make such autonomous UAVs for commercial usage in a time when everyone needs to take precautions to protect themselves against COVID-19.