From 5e5082a917780fff86813b4480a660f5714058f3 Mon Sep 17 00:00:00 2001 From: batuhanaavci Date: Sun, 10 Nov 2024 01:07:40 +0200 Subject: [PATCH] minor change --- _portfolio/portfolio-1.md | 16 ++-------------- _portfolio/portfolio-2.md | 7 +------ 2 files changed, 3 insertions(+), 20 deletions(-) diff --git a/_portfolio/portfolio-1.md b/_portfolio/portfolio-1.md index 727462aeb59a7..8169dba932866 100644 --- a/_portfolio/portfolio-1.md +++ b/_portfolio/portfolio-1.md @@ -4,20 +4,8 @@ excerpt: "PID, LQR, and energy-based swing-up LQR on Quanser Qube Servo 2.
collection: portfolio --- -This project was done as part of the course ELEC-E8004 Project Work course at Aalto University. The primary objective of this project was to develop and implement updated lab assignments and pre assignments for the control labs in the ELEC-C1310: Laboratory Exercises in Automation and Control Engineering course at Aalto University. Leveraging the Quanser Qube Servo 2 system, the project aims to provide students with practical, hands-on experience in control. On top of the course content, the scope is extended to non-linear control, and an energy-based swing-up combined with an LQR for balancing has been implemented. +This project was done as part of the course ELEC-E8004 Project Work course at Aalto University. The primary objective of this project was to develop and implement updated lab assignments and pre assignments for the control labs in the ELEC-C1310: Laboratory Exercises in Automation and Control Engineering course at Aalto University. Leveraging the Quanser Qube Servo 2 system, the project aims to provide students with practical, hands-on experience in control. On top of the course content, the scope is extended to non-linear control, and an energy-based swing-up combined with an LQR for balancing has been implemented. Below is a video demonstrating the implementation of control methods on the Quanser Qube Servo 2: + -## Video Demonstration -Below is a video demonstrating the implementation of control methods on the Quanser Qube Servo 2: - -[![Quanser Qube Servo 2 Video](https://img.youtube.com/vi/YevSQ600GKA/0.jpg)](https://www.youtube.com/watch?v=YevSQ600GKA) - -## Images - - \ No newline at end of file diff --git a/_portfolio/portfolio-2.md b/_portfolio/portfolio-2.md index 844bbabcc18ae..bcfb5a0403e66 100644 --- a/_portfolio/portfolio-2.md +++ b/_portfolio/portfolio-2.md @@ -4,12 +4,7 @@ excerpt: "Participated in MATLAB and Simulink Challenge Projects program. Extend collection: portfolio --- -This work presents a distributed system architecture that leverages the asynchronous threading and communication property of ROS2 to develop and implement a real-time efficient Deep Learning (DL) based method for recognizing and tracking a person of interest. The DL model receives snapshots from the quadcopter's camera and sends back an information vector, which includes all recognized persons and their corresponding position information within the camera frame of the quadcopter. The person of interest tracking control system receives face set information about the person of interest and generates reference velocity signals to be tracked by low-level controllers embedded within the drone. Experiments conducted in a cluttered and complex environment demonstrate the efficiency of the DL-based architecture for quadcopters. - - -## Video Demonstration - -Below is a video demonstrating our work: +This work presents a distributed system architecture that leverages the asynchronous threading and communication property of ROS2 to develop and implement a real-time efficient Deep Learning (DL) based method for recognizing and tracking a person of interest. The DL model receives snapshots from the quadcopter's camera and sends back an information vector, which includes all recognized persons and their corresponding position information within the camera frame of the quadcopter. The person of interest tracking control system receives face set information about the person of interest and generates reference velocity signals to be tracked by low-level controllers embedded within the drone. Experiments conducted in a cluttered and complex environment demonstrate the efficiency of the DL-based architecture for quadcopters. Below is a video demonstrating our work: