Terrain surveying using unmanned aerial vehicles (UAV) is being applied to many areas such as precision agriculture or battlefield surveillance. A core problem of the surveying task is the coverage path planning problem (CPP); it is defined as the task of determining a path for an unmanned aerial vehicle so that it observes all points of a target area. State-of-the-art planners solve the problem for a unique region. However, in some operations, for example search and rescue, it is important to plan a path that covers more than a single area. We propose a method for solving the CPP for disjoint areas. The general problem is modeled as a rural postman problem which has been demonstrated to be NP-Hard. Our solution relies on dividing the problem into two steps: optimization of the visiting order and optimization of the flight lines orientation. The method is validated through several simulations using real parameters. In addition, it is fast enough for being implemented onboard.

Robotics is a fast developing and exciting field with multiple applications which span from robot manipulators to mobile robots. This paper presents a literature review on wheeled mobile robots. We point out the main considerations to be taken into account when designing this class of robots. The main control problems are recalled as well as the corresponding solutions. Finally, we summarize the most important approaches used to render mobile robots autonomous.

A Micro Artificial Immune System

Apples are among the most important agricultural products worldwide. Ensuring apple quality with minimal effort is crucial for both large-scale and local producers. In Mexico, the manual detection of damaged apples has led to inconsistencies in product quality, a problem that can be addressed by integrating vision systems with machine learning algorithms. The YOLO (You Only Look Once) neural network has significantly improved fruit detection through image processing and has automated several related tasks. However, training and deploying YOLO models typically requires substantial computational resources, making it essential to develop lightweight and cost-effective detection systems, especially for edge computing systems. This paper presents a mechatronic system designed to detect apple varieties and potential damage in apples (Malus domestica) within the visible spectrum. The cultivated apple varieties considered were Gala, Golden, Granny Smith, and Red Delicious. Our contribution lies in developing a lightweight neural network architecture optimized specifically for embedded systems. The proposed architecture was compared against YOLOv3-Tiny, YOLOv4-Tiny, and YOLOv5-s. Our optimized model achieved a high accuracy and sensitivity (94–99%) and was successfully implemented on a Jetson Xavier NX board, where it reached a processing speed of 37 FPS.