Position detection system of mobile robot in the warehouse enviroment

Related tags

Deep LearningAutonomous_Forklift_System
Overview
MainwindowGUI  

Autonomous-Forklift-System

Top language Status Repository size

About   |   GUI   |   Tests   |   Starting   |   License   |   Author   |  


🎯 About

An application that run the autonomous forklift paletization aglorithms. Projekt was created for Engineering Thesis. It was fully completed. The last release of this application is available in this repository. Alication uses image processing (OpenCV library) to detect the position of Acuco markers. Based on the calculated positions of the forklift, pallets and the storage place, the trajectories for the truck are generated (mainly using Voronoi graph and Dijsktra algorithm). Then the module responsible for communication with the robot controls it, so the robot collects choosed pallets and puts them one on top of the other in the designated warehouse.

🖥️ GUI

The interface was created using the PyQt5 and pyqtgraph libraries.The following elements are shown in the figure:

  1. Menu bar - user can change the FPTV coefficient, communication port and baudrate.
  2. Image transmission - user can choose between three views. The first view displays the real image from the camera. View 2 displays marked tags with their identifiers. The third view displays the trajectories for a robot and updates it with each move robot (fig. 3.22).
  3. Entering identifiers - to start system, the user has to enter the robot ID and the pallet that the robot has deliver to the storage area.
  4. Parameters - In the Module statuses table you can observe camera, communication and navigation module status. The Warehouse table shows the status graphically storage place. The Parameters table shows in sequence: camera number (by default 1), COM port (COM14 by default), baud rate (9600 by default), ratio FPTV (25 by default), fault information, phase one status, phase two status, and the number of steps remaining to complete the phase.
  5. Start and progress button that starts the palletization process (Start). It is available only after the correct robot and pallette identifiers are entered

🔬 Tests

  1. Correctness of generated paths - The correct route is considered to be a safe route to the destination. In the first phase the palette is the goal. The robot has to reach the pallet and then pick it up. In the second phase the goal is the storage area (warehouse). Result: Correct

  1. Correctness of completing the route - A correctly covered route is characterized by a minimal error between generated route and the route taken by the robot. This is equivalent to maintenance safety assumptions for every traffic. Result: Correct

  1. Correctness of multi-level storage - This test checks whether the palletiser is correctly stacking the pallets. It is necessary to consider the created system as a multi-level palletizing system. Result: Correct

🏁 Starting

# Clone this project
$ git clone https://github.com/KamilGos/Autonomous_Forklift_System

# Access
$ cd Autonomous_Forklift_System

# Run the project
$ sudo python3 main.py

📝 License

This project is under license from MIT. For more details, see the LICENSE file.

🧑‍💻 Author

Made with ❤️ by Kamil Goś

 

Back to top

You might also like...
MOT-Tracking-by-Detection-Pipeline - For Tracking-by-Detection format MOT (Multi Object Tracking), is it a framework that separates Detection and Tracking processes?
MOT-Tracking-by-Detection-Pipeline - For Tracking-by-Detection format MOT (Multi Object Tracking), is it a framework that separates Detection and Tracking processes?

MOT-Tracking-by-Detection-Pipeline Tracking-by-Detection形式のMOT(Multi Object Trac

KazuhitoTakahashi 41 Nov 23, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 8, 2023
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 5, 2023
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 19.3k Feb 12, 2021
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

Documentation | FAQ | Release Notes | Roadmap | MACE Model Zoo | Demo | Join Us | 中文 Mobile AI Compute Engine (or MACE for short) is a deep learning i

Xiaomi 4.7k Dec 29, 2022
The UI as a mobile display for OP25
The UI as a mobile display for OP25

OP25 Mobile Control Head A 'remote' control head that interfaces with an OP25 instance. We take advantage of some data end-points left exposed for the

Sarah Rose Giddings 13 Dec 28, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Master Docs License Apache MXNet (incubating) is a deep learning framework designed for both efficiency an

ROCm Software Platform 29 Nov 16, 2022
Spatial Intention Maps for Multi-Agent Mobile Manipulation (ICRA 2021)
Spatial Intention Maps for Multi-Agent Mobile Manipulation (ICRA 2021)

spatial-intention-maps This code release accompanies the following paper: Spatial Intention Maps for Multi-Agent Mobile Manipulation Jimmy Wu, Xingyua

Jimmy Wu 70 Jan 2, 2023
Spatial Action Maps for Mobile Manipulation (RSS 2020)
Spatial Action Maps for Mobile Manipulation (RSS 2020)

spatial-action-maps Update: Please see our new spatial-intention-maps repository, which extends this work to multi-agent settings. It contains many ne

Jimmy Wu 27 Nov 30, 2022
Releases(v1.0.0)
Owner
Kamil Goś
Make my code with :heart:
Kamil Goś
GitHub Repository
Air Pollution Prediction System using Linear Regression and ANN

AirPollution Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living Publication Link:

Dr Sharnil Pandya, Associate Professor, Symbiosis International University 19 Feb 07, 2022
A highly efficient and modular implementation of Gaussian Processes in PyTorch

GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian

3k Jan 02, 2023
End-to-End Speech Processing Toolkit

ESPnet: end-to-end speech processing toolkit system/pytorch ver. 1.3.1 1.4.0 1.5.1 1.6.0 1.7.1 1.8.1 1.9.0 ubuntu20/python3.9/pip ubuntu20/python3.8/p

ESPnet 5.9k Jan 04, 2023
SASM - simple crossplatform IDE for NASM, MASM, GAS and FASM assembly languages

SASM (SimpleASM) - простая кроссплатформенная среда разработки для языков ассемблера NASM, MASM, GAS, FASM с подсветкой синтаксиса и отладчиком. В SA

Dmitriy Manushin 5.6k Jan 06, 2023
Single-Stage 6D Object Pose Estimation, CVPR 2020

Overview This repository contains the code for the paper Single-Stage 6D Object Pose Estimation. Yinlin Hu, Pascal Fua, Wei Wang and Mathieu Salzmann.

CVLAB @ EPFL 89 Dec 26, 2022
Dense Prediction Transformers

Vision Transformers for Dense Prediction This repository contains code and models for our paper: Vision Transformers for Dense Prediction René Ranftl,

Intelligent Systems Lab Org 1.3k Jan 02, 2023
A tf.keras implementation of Facebook AI's MadGrad optimization algorithm

MADGRAD Optimization Algorithm For Tensorflow This package implements the MadGrad Algorithm proposed in Adaptivity without Compromise: A Momentumized,

20 Aug 18, 2022
NDE: Climate Modeling with Neural Diffusion Equation, ICDM'21

Climate Modeling with Neural Diffusion Equation Introduction This is the repository of our accepted ICDM 2021 paper "Climate Modeling with Neural Diff

Jeehyun Hwang 5 Dec 18, 2022
Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

Utkarsh Agiwal 1 Feb 03, 2022
AI pipelines for Nvidia Jetson Platform

Jetson Multicamera Pipelines Easy-to-use realtime CV/AI pipelines for Nvidia Jetson Platform. This project: Builds a typical multi-camera pipeline, i.

NVIDIA AI IOT 96 Dec 23, 2022
QKeras: a quantization deep learning library for Tensorflow Keras

QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa

Google 437 Jan 03, 2023
Baseline for the Spoofing-aware Speaker Verification Challenge 2022

Introduction This repository contains several materials that supplements the Spoofing-Aware Speaker Verification (SASV) Challenge 2022 including: calc

40 Dec 28, 2022
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

PySlowFast PySlowFast is an open source video understanding codebase from FAIR that provides state-of-the-art video classification models with efficie

Meta Research 5.3k Jan 03, 2023
This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

The-Emergence-of-Objectness This is the official released code for our paper, The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

44 Oct 08, 2022
PyTorch Implementation of Google Brain's WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis

WaveGrad2 - PyTorch Implementation PyTorch Implementation of Google Brain's WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis. Status (202

Keon Lee 59 Dec 06, 2022
labelpix is a graphical image labeling interface for drawing bounding boxes

Welcome to labelpix 👋 labelpix is a graphical image labeling interface for drawing bounding boxes. 🏠 Homepage Install pip install -r requirements.tx

schissmantics 26 May 24, 2022
CC-GENERATOR - A python script for generating CC

CC-GENERATOR A python script for generating CC NOTE: This tool is for Educationa

Lêkzï 6 Oct 14, 2022
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and ap

3.4k Jan 04, 2023
Run Keras models in the browser, with GPU support using WebGL

**This project is no longer active. Please check out TensorFlow.js.** The Keras.js demos still work but is no longer updated. Run Keras models in the

Leon Chen 4.9k Dec 29, 2022
PyTorch implementation of our paper: Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition

Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition, arxiv This is a PyTorch implementation of our paper. 1. Re

DamoCV 11 Nov 19, 2022