Robotics | Khaled HAMIDI

Kenobot

Mon, 06 Jan 2025 00:00:00 +0000

Overview

Kenobot is an integrated robotic platform engineered as a commercial solution for autonomous navigation and robotics development. The platform’s design is flexible and scalable, making it suitable for a wide range of applications, from internal logistics to advanced research.

Dual-Layer Control Architecture

Kenobot utilizes a two-tier control architecture for high performance and reliability:

High-Level Control: A Raspberry Pi 5 computer processes complex sensor data, implements autonomous navigation algorithms, and manages the system.
Low-Level Control: An Arduino Mega board provides precise control of motors and actuators, and reads raw data from low-level sensors, ensuring a fast and stable response.

A key feature of Kenobot is its ability to navigate accurately in indoor environments where GPS signals are unavailable. The platform uses advanced algorithms like SLAM (Simultaneous Localization and Mapping) with data from sensors such as LIDAR and cameras to independently determine its location and map its surroundings.

Communication System

The platform supports a bidirectional wireless communication system for exchanging data and commands between the robot and a control station. The receiver unit connects to a computer via a USB port for monitoring and control.

Virtual Development Features

To enable developers to test algorithms and applications safely, each platform is equipped with virtual dummy features. This virtual environment simulates various tasks and payloads without requiring physical hardware, accelerating the development cycle and reducing risks.

Potential Applications

Security and Surveillance: Conducting autonomous patrols in designated areas.
Research and Development: A robust testbed for AI and robotics algorithms.

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Kenobot: A Self-Driving Robotic Platform

Mon, 06 Jan 2025 00:00:00 +0000

Overview

Dual-Layer Control Architecture

Kenobot utilizes a two-tier control architecture for high performance and reliability:

High-Level Control: A Raspberry Pi 5 computer processes complex sensor data, implements autonomous navigation algorithms, and manages the system.
Low-Level Control: An Arduino Mega board provides precise control of motors and actuators, and reads raw data from low-level sensors, ensuring a fast and stable response.

Communication System

Virtual Development Features

Potential Applications

Security and Surveillance: Conducting autonomous patrols in designated areas.
Research and Development: A robust testbed for AI and robotics algorithms.

Forward and Inverse Kinematics Analysis and Simulation in MATLAB of a 6R Industrial Robot

Thu, 05 Dec 2024 00:00:00 +0000

Introduction

This study focuses on the Elfin E15, a 6-DOF industrial robot from Han’s Robot, composed of six revolute joints. The analysis employs the Denavit-Hartenberg (D-H) parameters to model the robot’s kinematics.

D-H Parameters

The D-H parameter table is crucial for defining the robot’s structure, including link lengths and joint orientations.

Table 1: Elfin E15 Robot Arm Link Coordinate Parameters

Link	α_(i-1)	a_(i-1)	d_i	θ_i	Joint Variable
1	0	0	h_1	θ_1	θ_1
2	90	0	0	θ_2	θ_2
3	0	a_2	0	θ_3	θ_3
4	-90	0	d_4	θ_4	θ_4
5	+90	0	0	θ_5	θ_5
6	-90	0	d_6	θ_6	θ_6

Table 2: Link Lengths (mm)

Link	Length
h_1	262
a_2	580
d_4	520
d_6	173

Forward Kinematics Analysis

Forward kinematics determines the end effector’s position and orientation from given joint angles. This is achieved by computing transformation matrices between adjacent joint frames.

MATLAB Simulation

A MATLAB App Designer application was developed to simulate the robot’s motion. The robot_fkine function calculates the cumulative transformation matrices and visualizes the robot in a 3D plot.

Inverse Kinematics Analysis

Inverse kinematics calculates the required joint angles to achieve a desired end effector position and orientation. This is a more complex problem, often with multiple solutions.

Motion Simulation

The robot’s trajectory is planned by interpolating between initial and final joint configurations. Forward kinematics is used to calculate the end effector’s path, which is then animated.

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DESIGN AND MANUFACTURING OPEN SOURCE FILLING MACHINE

Wed, 01 May 2024 00:00:00 +0000

DESIGN AND MANUFACTURING OPEN SOURCE FILLING MACHINE FOR FIXED GRANULATE QUANTITIES

Video

This video explains how the machine works.

Publication Information

Journal: International Journal of Advanced Natural Sciences and Engineering Researches
Year: 2024
Volume: 8 Issue: 4 Pages: 122-128

Abstract

This article eliminates the need for sensors used in adjusting the measurement amount. A filling machine that quantifies ingredients using a mechanical mechanism that lifts offers design and manufacturing. The machine is suitable for coarse or granular with capacity up to 250ml customized to suit the materials and controlled by an electro-pneumatic system. Couple An upright cylindrical container moved by an effective cylinder was used. Container on top It is filled directly from the tank through the opening and when emptied, it is displaced horizontally and moves into the tank. Due to the weight of the materials, the bottom lid opens, which causes the container to be filled. One The conveyor belt was designed and operated by a DC motor using a driver circuit. Process, by an Arduino uno microcontroller with an infrared (IR) sensor and conveyor belt system It is automated. The opening and closing movement of the cylinder is a 5/3 motor that receives a signal from the control unit. It is controlled by a solenoid valve. Additionally, the design of the machine is designed for accessibility and compatibility. is customized and all components are 3D printed on a 200x200 mm construction site or job site can be produced. The open source nature of the design and software includes improvements made by the community and encourages adaptations and collaborates for continuous improvement and innovation in automated packaging systems Promotes a caring environment. experiments on different materials have been evaluated positively.

System Components

This image shows the electric and pneumatic system.

The electric and pneumatic system

Autonomous Robot with Longitudinal Control

Thu, 18 Jan 2024 00:00:00 +0000

About the Project

An autonomous robot I designed and built entirely from scratch — from the mechanical frame to the control algorithm. Its key feature is a longitudinal control system based on a hybrid FLC-PID architecture, enabling independent navigation with precise speed and acceleration control.

Highlights

FLC-PID System: Combines fuzzy logic with PID for smooth, adaptive speed control.
Built from scratch: Frame design, component selection, wiring, and programming all done manually.
Dual validation: Simulated in MATLAB/Simulink, then deployed on Raspberry Pi hardware.
Real-time response: The robot adapts its behavior based on live sensor readings.

Filtering and Classification of Shapes Chromatically Using a 4-DOF Robotic Arm

Wed, 26 Jul 2023 00:00:00 +0000

Manual sorting and classification of products is slow, error-prone, and often takes place in unhealthy working conditions. Replacing that part of the line with a robotic arm makes sense across a range of industries.

This study presents a setup for color-based sorting of shapes using a four-degree-of-freedom robotic arm controlled by a Raspberry Pi. A camera feed runs through an image-processing pipeline that identifies the color of each incoming object, and the arm is then commanded to pick the object up and place it in the bin assigned to that color. The paper covers the mechanical design of the arm, the kinematics, the vision pipeline, and the test results from a continuous-flow scenario.

Publication

Publisher: Sham Center for Studies and Research Published: July 26, 2023

Read it

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Local Guide AI system

Mon, 12 Jun 2023 12:00:00 +0300

Project Overview

The “Local Guide” project aims to develop an intelligent navigation system for autonomous robots using artificial intelligence (AI) and a beacon server. The system leverages Python to create a robust algorithm that determines the optimal path for the robot to reach its destination, in this case, a parking lot.

The project’s core component is a Python-based system that communicates with a beacon server via an API. The beacon server provides real-time data on the robot’s location and the surrounding environment, enabling the Python system to calculate the most efficient route to the parking lot.

Key Features

AI-powered Path Planning: The Python system utilizes artificial intelligence algorithms to analyze the environmental data from the beacon server and determine the optimal path for the robot to reach the parking lot while avoiding obstacles and minimizing travel time.
Beacon Server Integration: The system seamlessly integrates with a beacon server, which acts as a central hub for tracking the robot’s location and gathering environmental data from various sensors and beacons.
API Communication: The Python system communicates with the beacon server via a well-defined API, enabling efficient data exchange and real-time updates on the robot’s location and surroundings.
Obstacle Avoidance: The AI algorithms take into account potential obstacles along the path, allowing the robot to navigate safely and efficiently through dynamic environments.
Scalability and Adaptability: The system is designed to be scalable and adaptable, making it suitable for deployment in various scenarios and environments, from indoor navigation to outdoor exploration.

Project Outcome

The “Local Guide” project successfully demonstrated the power of artificial intelligence and Python programming in developing intelligent navigation systems for autonomous robots. The system’s ability to leverage real-time data from the beacon server and calculate optimal paths has proven to be a valuable asset in enhancing the efficiency and autonomy of robotic navigation.

With its modular design and scalability, the “Local Guide” system has the potential to be adapted for various applications, from industrial automation to logistics and transportation. The project showcases the possibilities of combining cutting-edge technologies like AI, Python programming, and beacon servers to solve complex navigation challenges.

The client provided an excellent review , rating it 5 stars across various aspects:

Criteria	Rating
Professionalism in dealing	★★★★★
Communication and follow-up	★★★★★
Quality of work delivered	★★★★★
Experience in the project field	★★★★★
Delivery on time	★★★★★
Deal with him again	★★★★★

Excellent rating for the Window Control System project

For more details about this project, please visit the .

Window Control System

Mon, 12 Jun 2023 12:00:00 +0300

Project Overview

This project involved designing and implementing a window control system to protect homes from dust and sand during sandstorms, a common occurrence in Saudi Arabia. The system works by automatically closing the windows when a sandstorm is detected, preventing dust and sand from entering the home.

The system comprises sensors to detect sandstorms and a motor mechanism to open and close the windows. It also includes a notification feature that sends short messages to the homeowner’s phone when a sandstorm is detected and the windows are being closed.

Features

Sandstorm detection sensors
Automated window opening and closing mechanism
Real-time notification system for homeowners
Energy-efficient and low-maintenance design

Project Outcome

The project was successfully completed, and the client in Saudi Arabia was highly satisfied with the results. The window control system received an excellent rating for its effectiveness in protecting homes from sandstorms and its user-friendly features.

Client Review

The client provided an excellent review for the Window Control System project, rating it 5 stars across various aspects:

Criteria	Rating
Professionalism in dealing	★★★★★
Communication and follow-up	★★★★★
Quality of work delivered	★★★★★
Experience in the project field	★★★★★
Delivery on time	★★★★★
Deal with him again	★★★★★

Excellent rating for the Window Control System project

For more details about this project, please visit the .

project to read and record temperature and humidity

Fri, 01 Feb 2019 00:00:00 +0000

It is a project that I did while studying at the university for the microprocessors course.

The project consists of a digital temperature and humidity sensor SH10 and an Arduino Uno board.
A desktop application developed in the C# language.
Temperature and humidity are recorded in a database in XML file format.
A graph of the change can be displayed.

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Arduino Code
Visual Studio C# project
Sensor datasheet
powerpoint file for presentation (ARABIC)

Download package: not published yet.

Robotics | Khaled HAMIDI

Kenobot

Overview

Dual-Layer Control Architecture

GPS-Denied Autonomous Navigation

Communication System

Virtual Development Features

Potential Applications

Kenobot: A Self-Driving Robotic Platform

Overview

Dual-Layer Control Architecture

GPS-Denied Autonomous Navigation

Communication System

Virtual Development Features

Potential Applications

Forward and Inverse Kinematics Analysis and Simulation in MATLAB of a 6R Industrial Robot

Introduction

D-H Parameters

Forward Kinematics Analysis

MATLAB Simulation

Inverse Kinematics Analysis

Motion Simulation

Download

DESIGN AND MANUFACTURING OPEN SOURCE FILLING MACHINE

Video

Publication Information

Abstract

System Components

The electric and pneumatic system

Autonomous Robot with Longitudinal Control

About the Project

Highlights

Filtering and Classification of Shapes Chromatically Using a 4-DOF Robotic Arm

Publication

Read it

Download

Local Guide AI system

Project Overview

Key Features

Project Outcome

Window Control System

Project Overview

Features

Project Outcome

Client Review

project to read and record temperature and humidity

Download