AloshkaD

AI_activity_recognition-

AI_Agent_Trader

Multi AI Agent Collaboration for Trending Stock Financial Analysis

AndroidUAVGS

This project is for testing a simple Android App to connect to the Pixhawk

Autonomous_SDC

Find lane lines and draw a solid line to localize a self-driving car on the road. I used image processing techniques and Haugh transform

DroneTracking

leetcode_assistant

LeetCode Coding multi-agent AI Assistant with LangGraph and Graph reasoning

localGPT-Vision

Chat with your documents using Vision Language Models. This repo implements an End to End RAG pipeline with both local and proprietary VLMs

P5_object_tracking

This project was developed for identifying vehicles in a video stream. The project is a corner stone for a real time vehicle tracking algorithm that employ semantic pixel-wise methods. This project solves the tracking problem for the Udacity final project in a different way that the general approach presented in the course. Instead of using the HOG features and other features extracted from the color space of the images, we used the U-Net[1] which is a convolutional network for biomedical image segmentation.

Realtime_AI_Assistant

Code for my cutting-edge multi-agent systems with advanced cognitive capabilities: Architecting AI agents using multi-modal graph reasoning for enhanced decision-making. Implementing neural-symbolic approaches to emulate and augment human-like intelligence Designing predictive models to evaluate multiple future outcomes for optimal agent choices.

SDCar_software_p3

Train a car how to drive using human driving behavior. it uses tensorflow, keras, and image augmentation.

AloshkaD/AndroidUAVGS

This project is for testing a simple Android App to connect to the Pixhawk

AloshkaD/AndroidUAVPlanner

AloshkaD/ardupilot

APM Plane, APM Copter, APM Rover source

AloshkaD/CppMT

AloshkaD/cuav

CanberraUAV OBC code

AloshkaD/cvg_ardrone2_ibvs

We present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following objects from a multi-rotor UAV, without any dependence on GPS systems. Our proposed method tracks a user specified object continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people, occlusions and also fast moving objects; showing the robustness of the proposed systems against perturbations and illumination changes. Our experiments show that the system is able to track a great variety of objects present in suburban areas, among others: people, windows, AC machines, cars and plants. urls: http://www.vision4uav.eu/?q=following and http://robotics.asu.edu/ardrone2_ibvs/ .

AloshkaD/cvg_quadrotor_swarm

We present a cost-effective framework for the prototyping of vision-based quadrotor multi-robot systems, which core characteristics are: modularity, compatibility with different platforms and being flight-proven. The framework is fully operative, which works in simulation and in real flight tests of up to 5 drones, and was demonstrated with the participation in the 2013 International Micro Air Vehicle Indoor Flight Competition (Toulouse, France).

AloshkaD/droneshare

DroneShare is a shared repository of drone missions built using DroneKit-Cloud

AloshkaD/ekf-slam-matlab

A Simultaneous Localisation and Mapping simulation in MATLAB

AloshkaD/EXPdrones

AloshkaD/EyeTribe_test

Experiment and analysis code to assess the usefulness of the EyeTribe tracker in psychological research.

AloshkaD/fann

Official github repository for Fast Artificial Neural Network Library (FANN)

AloshkaD/flight

Flight code for MIT CSAIL Robot Locomotion Group flying-through-forests project

AloshkaD/LeapMotionServoBots

LeapMotionServoBots

AloshkaD/Lidar

Acquire ranges via I2C on LIDAR-Lite module, and trigger Raspberry Pi camera on events

AloshkaD/navio-initial-state

Python script to collect Navio IMU board data and stream to Initial State

AloshkaD/Object-Track-and-Follow

An On-Drone Dynamic Object Track and Follow Solution

AloshkaD/OpenTLD

Official source code for TLD

AloshkaD/piot-101

PiOT Workshop Materials

AloshkaD/SiK

Tools and firmware for the Si1000

AloshkaD/STRUCK

AloshkaD/Tower

Ground Control Station for Android Devices

AloshkaD/vatic

vatic is an online, interactive video annotation tool for computer vision research that crowdsources work to Amazon's Mechanical Turk. Our tool makes it easy to build massive, affordable video data sets. Written in Python + C + Javascript, vatic is free and open-source software.