OpenScout is an Open Source Hardware (OSH) mobile robot for research and industry. It is extended to v1.1 which includes simplified, cheaper and more powerful onboard compute hardware; a simulated ROS2 interface; and a Gazebo simulation. Changes, their rationale, project methodology, and results are reported as an OSH case study.

The recent strides in artificial intelligence (AI) and machine learning (ML) have propelled the rise of TinyML, a paradigm enabling AI computations at the edge without dependence on cloud connections. While TinyML offers real-time data analysis and swift responses critical for diverse applications, its devices' intrinsic resource limitations expose them to security risks. This research delves into the adversarial vulnerabilities of AI models on resource-constrained embedded hardware, with a focus on Model Extraction and Evasion Attacks. Our findings reveal that adversarial attacks from powerful host machines could be transferred to smaller, less secure devices like ESP32 and Raspberry Pi. This illustrates that adversarial attacks could be extended to tiny devices, underscoring vulnerabilities, and emphasizing the necessity for reinforced security measures in TinyML deployments. This exploration enhances the comprehension of security challenges in TinyML and offers insights for safeguarding sensitive data and ensuring device dependability in AI-powered edge computing settings.

Internet of Things (IoT) devices are increasingly pervasive and essential components in enabling new applications and services. However, their widespread use also exposes them to exploitable vulnerabilities and flaws that can lead to significant losses. In this context, ensuring robust cybersecurity measures is essential to protect IoT devices from malicious attacks. However, the current solutions that provide flexible policy specifications and higher security levels for IoT devices are scarce. To address this gap, we introduce T800, a low-resource packet filter that utilizes machine learning (ML) algorithms to classify packets in IoT devices. We present a detailed performance benchmarking framework and demonstrate T800's effectiveness on the ESP32 system-on-chip microcontroller and ESP-IDF framework. Our evaluation shows that T800 is an efficient solution that increases device computational capacity by excluding unsolicited malicious traffic from the processing pipeline. Additionally, T800 is adaptable to different systems and provides a well-documented performance evaluation strategy for security ML-based mechanisms on ESP32-based IoT systems. Our research contributes to improving the cybersecurity of resource-constrained IoT devices and provides a scalable, efficient solution that can be used to enhance the security of IoT systems.

Humans and robots working together in an environment to enhance human performance is the aim of Industry 5.0. Although significant progress in outdoor positioning has been seen, indoor positioning remains a challenge. In this paper, we introduce a new research concept by exploiting the potential of indoor positioning for Industry 5.0. We use Wi-Fi Received Signal Strength Indicator (RSSI) with trilateration using cheap and easily available ESP32 Arduino boards for positioning as well as sending effective route signals to a human and a robot working in a simulated-indoor factory environment in real-time. We utilized machine learning models to detect safe closeness between two co-workers (a human subject and a robot). Experimental data and analysis show an average deviation of less than 1m from the actual distance while the targets are mobile or stationary.

This tutorial covers how to use Tensorflow micro speech with ESP32 with an external microphone I2S. In other words, we want to customize the Tensorflow micro speech example so that it runs on an ESP32 connected to an external microphone using the I2S protocol. In this example, we will use the INMP441 connected to the ESP32 to capture the audio. While the ESP32-EYE has a built-in microphone, if we want to use the Tensorflow micro speech with the ESP32 we need an external microphone that supports the I2S. Moreover, in this tutorial, we will use a custom model so that the ESP32 with INMP441 can recognize not only the yes or no words but other words too.

Run TensorFlow Lite Micro on the ESP32 Wi-Fi MCU. This example runs person detection on the ESP-EYE and emails the detected image.

The first thing you'll want to do is install PlatformIO. Now, create your project's root directory. This directory should also contain sub-directories for src, lib, and include. Within your project's root directory, create a file named platformio.ini. This file will contain all of the information needed for PlatformIO to initialize your development environment.