## 1 Introduction
Applications with ML and DL require particular hardware characteristics compared to traditional systems. Large-scale data processing, even with pretrained models on a computer, demands hardware with high performance. The problem has arisen in the last five years, and it has become a subbranch of the Artificial Intelligence of Things (AIoT) [15; 19; 20] that deals with the implementation of ML and DL algorithms in low-cost embedded systems known as TinyML and Tiny Deep Learning (TinyDL). TinyML is conceptually defined as an implementation trend in which traditional algorithms to achieve data processing using classical ML theory are migrated, to be programmed and executed, to Tiny embedded systems in which hardware features are much more limited than the computers and the traditional high-cost embedded systems [21]. This process involves the optimization of many algorithms related to ML and DL, such as pattern recognition and classification, linear regression, and sensor values prediction, among others. TinyML algorithm optimization, including DL, has emerged as a new research area in ML and AIoT. The research community in the mentioned field investigates how to run ML and DL algorithms satisfactorily within Tiny devices and hardware limitations [22]. The main contribution of this work is to provide the research community in the field of computer vision and AIoT, through a state-of-the-art review, with a specific overview of the key hardware and software characteristics that low-cost, hardware-constrained embedded systems must have to enable a basic implementation commonly used in mobile robotics applications. This implementation consists of a real-time image type classification using TinyML and is explained in detail in Section 3. The implementation using TinyML techniques is carried out to demonstrate the feasibility of low-cost hardware-constrained embedded systems for robotic applications using embedded vision. Likewise, in the related work section, the characteristics of each type of hardware and the optimization possibilities of ML and DL algorithms are investigated. In addition, we include an overview of algorithms that allow improvement of computer vision performance using low-cost hardware-constrained embedded systems and the key tools for optimal algorithm development in TinyML applications. This work is divided as follows: This section shows the introduction and the most important concepts in this review. The related work is presented and discussed in Section 2. The discussions and analysis include the advantages and disadvantages of each system based on its hardware architecture and features, vision system, power consumption, and software support. In Section 3, a TinyML implementation for robotics applications using embedded computer vision is presented. A deep neural network (DNN) is especially implemented for object classification on a Tiny device using the TensorFlow Lite and Keras library in Python for training in a computer, as well as the Arduino IDE for Tiny device programming. Raspberry Pi Pico, ESP32, and Arduino Nano 33 BLE Sense devices execute a DNN using the Iris database. In Section 4, a projected future trend for low-cost embedded vision systems for robotics applications is addressed. In Section 5, the conclusions are presented.