Research and Documentation of My Work in Robotics and Computing

My Projects.

Robotic Sensor Design

Tactile Sensing Artificial Fingertip for Prosthetics and Humanoids

I developed a high-density tactile fingertip capable of sensing pressure and vibration in a compact form factor. It was designed for humanoid robotic hands and can also be applied in prosthetic fingers, developed as part of my research in tactile sensing.

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High-Density Tactile Sensing Artificial Fingertip for Humanoids and Prosthetics

This project focuses on the design and development of a high-density tactile fingertip module intended for humanoid robot and prosthetic hand applications. The module is capable of measuring pressure exerted on its surface through 22 individual pressure sensors distributed across the fingertip area. It is developed using Flexible Printed Circuit Board (Flex-PCB) technology.

In this project, an origami technique is employed to fold a flat, paper-like flexible tactile sensing module into a three-dimensional shape that mimics the form of a human fingertip. Sensor data acquisition is handled by an onboard 32-bit, low-power microcontroller, and the module communicates using the SPI protocol.

In this project, an origami technique is employed to fold a flat, paper-like flexible tactile sensing module into a three-dimensional shape that mimics the form of a human fingertip. Sensor data acquisition is handled by an onboard 32-bit, low-power microcontroller, and the module communicates using the SPI protocol.

The development of this module presented a number of challenges, particularly in mechanical design and electronics. Prior to this project, I had limited experience with mechanical Computer-Aided Design (CAD). I had to learn how to design both mechanical structures and electronic schematics, as well as manage the manufacturing and integration processes.

Despite these challenges, a functional prototype of the tactile fingertip was developed. While the current version is not yet optimized for production, it is capable of capturing tactile data. Ongoing work includes further optimization of the design, data collection, and analysis.

The dimensions of the fingertip module closely match those of a typical human fingertip, enabling applications in humanoid robots and prosthetic systems.

CAD Model

Figure 2: The CAD model on the left illustrates the structural design and assembly of the high-density tactile fingertip module. It was created using Siemens Solid Edge, leveraging the sheet metal environment to model the foldable geometry of the flexible PCB. The part environment was used to design and integrate additional components, including pressure sensors and the onboard microcontroller.

Board Layout

The schematics and board layout of the tactile fingertip module were designed using Autodesk Eagle EDA. It was a challenging task, as a large number of vias had to be managed within a very small area. The board layout includes approximately 218 vias on a two-layer flexible PCB, with dimensions around 32 mm × 74 mm.

Figure 3: Board layout of the tactile fingertip module.

Fully Populated Tactile Fingertip Module

The tactile fingertip module is manufactured using 2-layer flexible PCB technology with a thickness of 0.11 mm. After fabrication, surface-mount technology (SMT) is used to populate the electronic components. Figure 4 depicts the fully populated tactile fingertip flexible PCB placed next to a 10-cent coin, highlighting the size difference and the complexity of the design.

Figure 4: Fully fabricated and populated tactile fingertip module in flat form factor.

Origami

A technique called origami, a traditional Japanese art of folding paper to form intricate shapes, is applied here to transform the flat version of the flexible tactile fingertip module into a 3D shape resembling a fingertip. Although origami is a complex art and I don't have much expertise in it, I attempted a simplified version to apply the technique as best as I could. The figure below shows the fingertip after being folded using origami techniques, including both the top (Figure 5a) and bottom (Figure 5b) views. The bottom view represents the fleshy part of the fingertip.

Figure 5: Top (a) and Bottom (b) views of the tactile fingertip module folded into a 3D humanoid fingertip-like shape using origami techniques.

Tactile Fingertip on a Human Finger

Figure 6: Shows the high-density tactile fingertip module mounted on a human finger. This demonstration is for illustrative purposes only. While the module is designed for integration into a humanoid robot hand, I did not have access to the robot hand toward the end of the project. I plan to update this demonstration with a robotic hand implementation in the near future.

Future Work

Further work on data collection, analysis, and system optimization is planned. In future updates, I aim to include additional documentation such as:

Detailed experimental results
Data analysis and sensor performance evaluations

This project will likely continue to evolve as time allows. At the moment, my availability is limited due to involvement in other work. While I have clear plans for improvements, progress will depend on available time."

I haven't published the data, schematics, or full documentation yet, as I believe the project isn’t complete enough to share publicly. Once it reaches a point I consider satisfactory, I will make those materials available.

AI - Computer Vision

Real-Time Traffic Monitoring Using Darknet (YOLO)

Real-time traffic monitoring application that takes video streams and detects objects (vehicles, pedestrians, traffic signs) using a YOLO model implemented in the C programming language with the Darknet library, trained on the Microsoft COCO dataset.

More Info GitHub

AI - Computer Vision

Handwritten Digit Recognition App Using CNN

An application that recognizes handwritten digits using a Convolutional Neural Network (CNN). The model is trained on the MNIST dataset and integrated into a user-friendly GUI built with Python.

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Handwritten Digit Recognition Application using CNN

A simple and intuitive application that recognizes handwritten digits using a Convolutional Neural Network (CNN). The model is trained on the MNIST dataset and deployed with a user-friendly PyTkinter GUI.

Getting Started

To get started with the Handwritten Digit Recognition App, follow these steps:

Clone the repository
Navigate to the project directory
Install the required dependencies
Run the application

Clone the Repository


git clone https://github.com/neoviki/ai.vision.handwritten.digit.recognizer.cnn.mnist.git
cd ai.vision.handwritten.digit.recognizer.cnn.mnist

Install Dependencies


chmod +x install.dependencies.sh
./install.dependencies.sh

Run the Application


chmod +x run.app.sh
./run.app.sh

Reference


@article{lecun2010mnist,
  title={MNIST handwritten digit database},
  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
  journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
  volume={2},
  year={2010}
}

For more details on this project, visit https://github.com/neoviki/ai.vision.handwritten.digit.recognizer.cnn.mnist

Computer Networks - IoT

IoT Data Transmission Suite

An IoT Data Transmission Suite with a server daemon and client library, designed for IoT applications. Traffic is transmitted over the HTTP/HTTPS protocol. It supports homegrown plant monitoring and vehicle status tracking, offering a cost-effective alternative to MQTT for lightweight, low-traffic communication. This suite is ideal for simple IoT ecosystems where frequent or intensive data exchange is not required.

GitHub

Embedded Controller

Tactile Controller

A 32-bit embedded controller module developed to efficiently retrieve and process tactile sensor data.

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Sensor Design

Tactile Sensing Module with a Dense Arrangement of Sensors

This tactile sensing module was developed to evaluate the application of closely arranged barometer sensors in tactile sensing.

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Other Projects

List of Other Projects I have Worked On

I’ve worked on a variety of projects based on my interests and the challenges I encountered along my journey. These projects span across AI, Computer Networking, Linux, GUI, Terminal tools, as well as Robotics and Electronics. I’ve documented and published them on GitHub. This section includes a list of those projects along with their corresponding GitHub URLs.

More Info GitHub

List of Projects

I've had the opportunity to work on projects across a range of fields, including telecom, vehicle telematics, IoT, computer vision, and robotic sensor design - covering areas such as computer science, electronics, and robotics. These projects were practical solutions to challenges I encountered in both day-to-day computing and industry environments. They include Linux utilities, drivers that optimize and streamline personal and industrial workflows, Artificial Intelligence driven applications and command-line tools using `awk`, `sed`, and regex for data splitting, filtering, manipulation, and interpretation. I've also developed C/Python libraries and packages, as well as custom hardware modules such as tactile sensors for robotic sensing.

Below is a list of selected projects, all of which I've published on GitHub.

Nr	Project Details	Reference
1	ai.vision.realtime.traffic.monitoring	Link
2	flexible.tactile.sensing.module.dual.sensors	Link
3	tactile.controller.module	Link
4	ai.vision.handwritten.digit.recognizer.cnn.mnist	Link
5	ai.vision.image.classifier.cnn.cifar10	Link
6	ai.ml.classification.knn	Link
7	ai.vision.face.detector	Link
8	linux.embed.dir.to.bash.script	Link
9	linux.over.the.air.update.utility	Link
10	linux.remote.debugger.utility	Link
11	linux.rpi.samba.file.share	Link
12	linux.rpi.vnc.server	Link
13	linux.rpi.wifi.access.point	Link
14	linux.wifi.monitor	Link
15	compress	Link
16	video.to.gif	Link
17	versionsnap	Link
18	graphical.interface.grid.eye.sensor.electron	Link
19	graphical.interface.gyroscope.sensor	Link
20	graphical.interface.robotic.gripper	Link
21	graphical.interface.serial.monitor	Link
22	iot.data.exchange.suite	Link
23	eagle.library.pic32mm0256gpm036.uqfn.40pin	Link
24	eagle.library.pic32mm0256gpm028.uqfn.28pin	Link
25	arduino.AMG8833.grid.eye.sensor	Link
26	arduino.Bosch.BMP384.pressure.sensor	Link
27	arduino.VL6180X.time.of.flight.distance.sensor	Link
28	arduino.esc.controller	Link
29	audio.joiner	Link
30	mp3.multiplier	Link
31	automate.ceph.distributed.storage.setup	Link
32	automate.transaction.on.ebay	Link
33	crop.image	Link
34	docker.wrapper.commands	Link
35	explainable.ai.evalution.app	Link
36	expose.tcp.port	Link
37	gps.coordinates.renderer.osm	Link
38	ipc.filelock	Link
39	ipc.messenger	Link
40	ipc.shared.memory	Link
41	mac.osx.tabrun	Link
42	netcopy	Link
43	proxmox.automation.utilities	Link
44	rand.file.tagger	Link
45	responsive.css.layouts	Link
46	shortest.path.finder.sim	Link
47	terminate	Link
48	translate.textfile	Link
49	uscreen	Link
50	video.splitter	Link
51	website.backup.and.restore.utility	Link
52	html.ui.generator	Link
53	ui.python.tkinter.wrapper	Link

About Me.

I am Viki (officially VN, representing the initials of my first and last name). By profession, I am a software architect specializing in embedded systems and IoT, with over a decade and a half of experience in the software industry, including nearly decade at a leading California-based computer networking company famously associated with the San Francisco Bridge (a hint, if you're curious about the company). In addition, I have worked as a consulting software architect and manager for more than five years, supporting clients across Europe and the UK. I have collaborated with and led teams of engineers from diverse ethnic and cultural backgrounds, including professionals from France, Belgium, the UK, Russia, and South Asia. I have built solutions including robotic sensors, artificial intelligence applications, embedded firmware, and Linux-based software for industries such as robotics, computer networking and telecommunications, IoT, and automotive engineering.

My academic background includes a master’s degree from Germany, specializing in Computer Science and Robotics, which provided me with a fundamental understanding of robotics, sensors, and the building blocks of artificial intelligence. During my studies, I had the opportunity to work with robots, from humanoids to autonomous vacuum cleaner robots, as well as in artificial intelligence areas such as computer vision and explainable AI.

One of my most significant achievements is the development of a high-density tactile fingertip module for prosthetic hands and humanoid robots, which is detailed on my research and projects page. In my home lab, I have created over 50 open-source projects, ranging from embedded hardware and AI applications to over-the-air update utilities, Linux utilities, and sensor modules, all of which are documented on my GitHub ( check my project list page).

I have a strong interest in computing, electronics, and robotics, and I enjoy researching their interdisciplinary applications, which can be applied to various domains to improve existing processes in the target domain, thereby optimizing both efficiency and cost.

My technical skills include programming in C and Python, and I have developed applications in various other languages, adapting to each based on project requirements. In addition to programming, I actively work on data analysis and organization using the awk programming language, as well as concepts like regular expressions and relational databases, which I frequently apply for debugging, data parsing, and structuring information.

Alongside my software and data analysis work, I have practical expertise in using the KiCAD EDA tool and CAD modeling with Solid Edge, particularly for embedded systems and robotic sensor module design.

Contact Me.

You can get in touch with me at hello@viki.design