## 5 Conclusions  
We reviewed the state of resource-constrained embedded vision systems-based TinyML for robotic applications, considering relevant aspects such as hardware features, power consumption, the integrated vision system, and the programming language. We provided an example of the basic implementation of the ML algorithm within an embedded system. From our review, the current challenges in robotics and embedded vision are focused on optimizing ML models that allow embedded systems to solve tasks more quickly and accurately, with less power consumption and minimizing the extensive use of each system's hardware resources. In the implementation carried out in this work, the Arduino environment is used to program source code and deploy it in embedded systems, and the training of the DNN TensorFlow Lite Framework in Python IDLE was required. Arduino IDE and Python are open-source platforms for developing and implementing the DNN algorithm, representing an advantage over other implementations reported in the state-of-the-art due to using environments with useful but licensing tools and expensive hardware architectures with a patent license. In this work, we used the Iris dataset. Current TinyML trends return to the Iris dataset, among other database types, to test the performance of an embedded system when running an inference, classification, and learning algorithm. In our case, the performance of the Pi Pico, ESP32, and Nano 33 BLE devices is tested by executing a DNN algorithm that calls a file containing the information of the trained model on the computer. The implementation stage allowed us to test the possibility of running ML algorithms on low-cost, hardware-constrained embedded systems. Regarding Table 16, we can establish the following conclusive points of the analysis:  
**Latency time: ESP32:** This microcontroller has acceptable performance for running AI models, although it may have slightly higher latencies due to its lower processing capacity compared to the Raspberry Pi Pico and Nano BLE. **Raspberry Pi Pico:** With its dual-core and higher clock speed, the Pico can handle inference tasks with slightly lower latency. **Nano BLE:** There is latency between the ESP32 and the Pico, balanced for inference tasks in embedded systems. **Energy consumed: ESP32:** Tends to consume more power due to its built-in Wi-Fi and Bluetooth, which may be active even if unnecessary for the specific task. **Raspberry Pi Pico:** Its simpler, more energy-efficient design results in lower power consumption during inference. **Nano BLE:** Intermediate power consumption, optimized for processing tasks and BLE connectivity. **Allocated RAM: ESP32:** Has more RAM available than the Pico, allowing us to allocate more memory to the model, although this may not be critical for a simple model like the Iris. **Raspberry Pi Pico:** With less RAM available, the Pico can handle simpler models or models optimized for resource-constrained devices. **Nano BLE:** Intermediate RAM, sufficient for most models trained with TensorFlow Lite. The analysis of the performance of the used DNN model is an aspect that will be addressed in future work. With regard to the selected embedded systems, it is possible to conclude the following:  
**ESP32:** Ideal if processing capacity and a low prediction error rate are prioritized, although at the cost of higher energy consumption. **Raspberry Pi Pico:** Best choice for applications that require low latency and low power consumption, with a slight trade-off in accuracy. **Arduino Nano BLE 33 Sense:** Balance between energy efficiency, processing capacity, and connectivity, suitable for applications where good performance with BLE connectivity is needed. In addition, future work includes studying other ML algorithms to solve more complex classification and regression problems in embedded computer vision and system control applications and using other embedded systems with enhanced hardware features to carry out different applications in TinyML and AloT. Conceptualization, M.B.-E. and T.E.A.