Position detection system of mobile robot in the warehouse enviroment

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? 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

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

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

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

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

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

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

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

Releases(v1.0.0)
Owner
Kamil Goś
Make my code with :heart:
Kamil Goś
VGG16 model-based classification project about brain tumor detection.

Brain-Tumor-Classification-with-MRI VGG16 model-based classification project about brain tumor detection. First, you can check what people are doing o

Atakan Erdoğan 2 Mar 21, 2022
U-Net: Convolutional Networks for Biomedical Image Segmentation

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

Yihui He 401 Nov 21, 2022
The PyTorch implementation of DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision.

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision The PyTorch implementation of DiscoBox: Weakly Supe

Shiyi Lan 1 Oct 23, 2021
Character Grounding and Re-Identification in Story of Videos and Text Descriptions

Character in Story Identification Network (CiSIN) This project hosts the code for our paper. Youngjae Yu, Jongseok Kim, Heeseung Yun, Jiwan Chung and

8 Dec 09, 2022
SMPL-X: A new joint 3D model of the human body, face and hands together

SMPL-X: A new joint 3D model of the human body, face and hands together [Paper Page] [Paper] [Supp. Mat.] Table of Contents License Description News I

Vassilis Choutas 1k Jan 09, 2023
A platform for intelligent agent learning based on a 3D open-world FPS game developed by Inspir.AI.

Wilderness Scavenger: 3D Open-World FPS Game AI Challenge This is a platform for intelligent agent learning based on a 3D open-world FPS game develope

46 Nov 24, 2022
Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

Official repository for GCR rerank, a GCN-based reranking method for both image and video re-ID

53 Nov 22, 2022
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

News December 27: v1.1.0 New loss functions: CentroidTripletLoss and VICRegLoss Mean reciprocal rank + per-class accuracies See the release notes Than

Kevin Musgrave 5k Jan 05, 2023
Let's Git - Versionsverwaltung & Open Source Hausaufgabe

Let's Git - Versionsverwaltung & Open Source Hausaufgabe Herzlich Willkommen zu dieser Hausaufgabe für unseren MOOC: Let's Git! Wir hoffen, dass Du vi

1 Dec 13, 2021
Bayesian dessert for Lasagne

Gelato Bayesian dessert for Lasagne Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the be

Maxim Kochurov 84 May 11, 2020
Code release for General Greedy De-bias Learning

General Greedy De-bias for Dataset Biases This is an extention of "Greedy Gradient Ensemble for Robust Visual Question Answering" (ICCV 2021, Oral). T

4 Mar 15, 2022
Convert Python 3 code to CUDA code.

Py2CUDA Convert python code to CUDA. Usage To convert a python file say named py_file.py to CUDA, run python generate_cuda.py --file py_file.py --arch

Yuval Rosen 3 Jul 14, 2021
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking Updates 08/2021: check out our domain adaptation for video segmentation paper Domain A

17 Nov 30, 2022
This is Official implementation for "Pose-guided Feature Disentangling for Occluded Person Re-Identification Based on Transformer" in AAAI2022

PFD:Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer This repo is the official implementation of "Pose-gui

Tao Wang 93 Dec 18, 2022
Differentiable Simulation of Soft Multi-body Systems

Differentiable Simulation of Soft Multi-body Systems Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin [Paper] [Code] Updates The C++ backend s

YilingQiao 26 Dec 23, 2022
Multi-task head pose estimation in-the-wild

Multi-task head pose estimation in-the-wild We provide C++ code in order to replicate the head-pose experiments in our paper https://ieeexplore.ieee.o

Roberto Valle 26 Oct 06, 2022
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation (ICCV2021)

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation This is a pytorch project for the paper Dynamic Divide-and-Conquer Ad

DV Lab 29 Nov 21, 2022
Implementation of OpenAI paper with Simple Noise Scale on Fastai V2

README Implementation of OpenAI paper "An Empirical Model of Large-Batch Training" for Fastai V2. The code is based on the batch size finder implement

13 Dec 10, 2021
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.

Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021. Bobo Xi, Jiaojiao Li, Yunsong Li and Qian Du. Code f

Bobo Xi 7 Nov 03, 2022