2DOF VR MOTION SIMULATOR - Oculus Rift & HTC VIVE

2DOF VR MOTION SIMULATOR - Oculus Rift & HTC VIVE

Project from the scratch, design, code, build construction and integration.

Robotics, Virtual Reality

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Description

I've got request from my client to build a motion simulator of car racing game. To be honest I never build even learn about motion simulator. Collect a lot of information from google and youtube as references to learn. Basically we can get 3D Motion Data from almost racing simulator games. I join the XSimulator.net community to get a lot of useful information and tutorial about Motion Simulator.

I use 2 PCs for this project. One as Gaming PC with high-specs and One as Game Controller that connect to Genuino101 via USB. Both PC connected via Crossed Ethernet Cable. Gaming PC Specs: Intel i7-6700 3.4GHz, 32GB DDR4, SSD 250GB, GTX1070 Video card, ASUS H170 Pro Gaming Motherboard. Game Controller PC Specs: Lenovo ThinkCentre M93p Mini PC, Intel i5-2.9GHz, 8GB DDR3, SSD250GB

I start from design sketch, then make a scaled model using 3D printing for learning the mechanism works and test the code and interconnection data from 2 pc. Continue drawing in 3D CAD to get detail size/dimension of metal parts.

Then start prepare the construction and build using MIG / CO Welding. I use Wiper Motor for Mercedes Benz 300E car as the actuator for motion simulator. And for driver, I use 43A BTS7960 DC Motor Driver and 30A 12V Switching Power Supply.

For Gaming Gear, I use Thrustmaster T500RS Steering Wheel and Pedals + Gear Shift. For Sound System, I use Razer Leviathan 5.1 Surround Sound Bar Dolby Speaker. The subwoofer placed on rig-base under the seat. I use 3 Samsung Curve 24" Monitor driven by GTX1070 multi display Video Adapter.

For game itself I choose Asseta Corsa and Project Cars. This project developed in two months.

Gallery

Video

Links

VR RACING SIMULATOR XPERIENCE - OCULUS RIFT

VIDEO: VR Motion Simulator in action 1

VIDEO: VR Motion Simulator in action 2

VIDEO: 2DOF MOTION SIMULATOR - VIRTUAL REALITY HTC VIVE

VIDEO: DJARUM BLACK XPERIENCE MOTION SIMULATOR

VIDEO: 2 Laps Racing: DJARUM BLACK XPERIENCE MOTION SIMULATOR

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Siddharth N. created project Gesture Recognition System for soldiers

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Gesture Recognition System for soldiers

It is a gesture classification system for soldiers where cameras cannot be used.The system I made can classify 40 of the standard army gesture.It uses a support vector machine to classify the gestures.I created my own dataset for training. The list of gestures contain static as well as dynamic gestures. Different algorithms in terms of number of features was used for classifying them. The list of gestures can be found here: https://www.zombiehunters.org/wiki/index.php/Military_Hand_Signals Further improvement will be made using wireless modules and increasing mobility.

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Burhan K. updated status

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Burhan KAYA

AI , Cyber Security, IOT

Hello I'm an Electrical and Electronics Engineer. I do research on artificial intelligence and IOT. Besides this, I have a project with artificial intelligence algorithms. Therefore, using MODIVUS will contribute to me. I do not want to be deprived of such an impossibility. I'm glad you helped me.

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Chaplin M. updated status

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Chaplin Marchais

Well its now 7am and I have been up for about 30 hours.... but the image recognition is now actually working in azure!! Now time to take a power nap and then do some optimization with Intel's awesome suite of tools!

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Chaplin M. updated status

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Chaplin Marchais

Fifth night in a row I find myself still in front of the computer at 2AM.... I think I got up at-least twice today though!! Progress... Oh well, the future doesn't build itself! ...... yet....

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Moloti N. created project Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

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Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

We propose the use of Drones to help communities enhance their security initiatives, to identify criminals during the day and at night. We use multiple sensors and computer vision algorithms to be able to recognize/detect motion and content in real-time, then automatically send messages to community members cell phones about the criminal activities. Hence, community members may be able to stop house breakings before they even occur.

Machine Intelligence Algorithm Design Methodology

AMCnet: https://github.com/AfricaMachineIntelligence/AMCnet https://devmesh.intel.com/projects/africa-motion-content-network-amcnet

We propose a deep neural network for the prediction of future frames in natural video sequences using CPU. To effectively handle complex evolution of pixels in videos, we propose to decompose the motion and content, two key components generating dynamics in videos. The model is built upon the Encoder-Decoder Convolutional Neural Network and Convolutional LSTM for pixel-level prediction, which independently capture the spatial layout of an image and the corresponding temporal dynamics. By independently modeling motion and content, predicting the next frame reduces to converting the extracted content features into the next frame content by the identified motion features, which simplifies the task of prediction. The model we aim to build should be end-to-end trainable over multiple time steps, and naturally learns to decompose motion and content without separate training. We evaluate the proposed network architecture on human AVA and UCF-101 datasets. We show state-of-the art performance in comparison to recent approaches. This is an end-to-end trainable network architecture running on the CPU with motion and content separation to model the spatio-temporal dynamics for pixel-level future prediction in natural videos.

// We then use this AMCnet pretrained model on the Video feed from the DJI Spark drone, integrated with the Movidius NCS to accelerate real-time object detection neural networks.

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