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
[CVPR 2021] Forecasting the panoptic segmentation of future video frames

Panoptic Segmentation Forecasting Colin Graber, Grace Tsai, Michael Firman, Gabriel Brostow, Alexander Schwing - CVPR 2021 [Link to paper] We propose

Niantic Labs 44 Nov 29, 2022
YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

YOLOX-Paddle A reproduction of YOLOX by PaddlePaddle 数据集准备 下载COCO数据集，准备为如下路径 /ho

QuanHao Guo 6 Dec 18, 2022
Request execution of Galaxy SARS-CoV-2 variation analysis workflows on input data you provide.

SARS-CoV-2 processing requests Request execution of Galaxy SARS-CoV-2 variation analysis workflows on input data you provide. Prerequisites This autom

useGalaxy.eu 17 Aug 13, 2022
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pu

Ritchie Ng 9.2k Jan 02, 2023
Multi-Scale Progressive Fusion Network for Single Image Deraining

Multi-Scale Progressive Fusion Network for Single Image Deraining (MSPFN) This is an implementation of the MSPFN model proposed in the paper (Multi-Sc

Kuijiang 128 Nov 21, 2022
PyTorch module to use OpenFace's nn4.small2.v1.t7 model

OpenFace for Pytorch Disclaimer: This codes require the input face-images that are aligned and cropped in the same way of the original OpenFace. * I m

Pete Tae-hoon Kim 176 Dec 12, 2022
This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive Selective Coding)

HCSC: Hierarchical Contrastive Selective Coding This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive

YUANFAN GUO 111 Dec 20, 2022
Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech"

GradTTS Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv) About this repo This is an unoffic

HeyangXue1997 103 Dec 23, 2022
Class-Attentive Diffusion Network for Semi-Supervised Classification [AAAI'21] (official implementation)

Class-Attentive Diffusion Network for Semi-Supervised Classification Official Implementation of AAAI 2021 paper Class-Attentive Diffusion Network for

Jongin Lim 7 Sep 20, 2022
A Broad Study on the Transferability of Visual Representations with Contrastive Learning

A Broad Study on the Transferability of Visual Representations with Contrastive Learning This repository contains code for the paper: A Broad Study on

Ashraful Islam 29 Nov 09, 2022
The Balloon Learning Environment - flying stratospheric balloons with deep reinforcement learning.

Balloon Learning Environment Docs The Balloon Learning Environment (BLE) is a simulator for stratospheric balloons. It is designed as a benchmark envi

Google 87 Dec 25, 2022
Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.

Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge: Official Pytorch implementation of ICLR 2018 paper Deep Learning for Phy

emmanuel 47 Nov 06, 2022
Neural Point-Based Graphics

Neural Point-Based Graphics Project   Video   Paper Neural Point-Based Graphics Kara-Ali Aliev1 Artem Sevastopolsky1,2 Maria Kolos1,2 Dmitry Ulyanov3

Ali Aliev 252 Dec 13, 2022
This repository contains the source codes for the paper AtlasNet V2 - Learning Elementary Structures.

AtlasNet V2 - Learning Elementary Structures This work was build upon Thibault Groueix's AtlasNet and 3D-CODED projects. (you might want to have a loo

Théo Deprelle 123 Nov 11, 2022
Learning to Self-Train for Semi-Supervised Few-Shot

Learning to Self-Train for Semi-Supervised Few-Shot Classification This repository contains the TensorFlow implementation for NeurIPS 2019 Paper "Lear

86 Dec 29, 2022
Planning from Pixels in Environments with Combinatorially Hard Search Spaces -- NeurIPS 2021

PPGS: Planning from Pixels in Environments with Combinatorially Hard Search Spaces Environment Setup We recommend pipenv for creating and managing vir

Autonomous Learning Group 11 Jun 26, 2022
Contrastive Learning for Compact Single Image Dehazing, CVPR2021

AECR-Net Contrastive Learning for Compact Single Image Dehazing, CVPR2021. Official Pytorch based implementation. Paper arxiv Pytorch Version TODO: mo

glassy 253 Jan 01, 2023
Deep learning operations reinvented (for pytorch, tensorflow, jax and others)

This video in better quality. einops Flexible and powerful tensor operations for readable and reliable code. Supports numpy, pytorch, tensorflow, and

Alex Rogozhnikov 6.2k Jan 01, 2023
Contextualized Perturbation for Textual Adversarial Attack, NAACL 2021

Contextualized Perturbation for Textual Adversarial Attack Introduction This is a PyTorch implementation of Contextualized Perturbation for Textual Ad

cookielee77 30 Jan 01, 2023
PyTorch implementation of MulMON

MulMON This repository contains a PyTorch implementation of the paper: Learning Object-Centric Representations of Multi-object Scenes from Multiple Vi

NanboLi 16 Nov 03, 2022