Abstract
Identifying the maturation stage is an added value for olive oil producers and consumers, whether this is done to predict the best harvest time, give us more information about the olive oil, or even adapt techniques and extraction parameters in the olive oil mill. In this way, the proposed work presents a new method to identify and count the number of olives that enter the mill as well as their stage of maturation. It is based on artificial intelligence (AI) and deep learning algorithms, using the two most recent versions of YOLO, YOLOv7 and YOLOv8. The obtained results demonstrate the possibility of using this type of application in a real environment, managing to obtain a mAP of approximately 79% with YOLOv8 in the five maturation stages, with a processing rate of approximately 16 FPS increasing this with YOLOv7 to 36.5 FPS reaching a 66% mAP.

Abstract
The article provides a comparison of Convolutional Neural Network (CNN) and Reinforcement Learning (RL) applied to the field of autonomous driving within the CARLA (CAr Learning to Act) simulator for training and evaluation. The analysis of results revealed CNNs better overall performance, as it demonstrated a more refined driving experience, shorter training durations, and a more straightforward learning curve and optimization process. However, it required data labelling. In contrast, RL relayed on an exhaustive (unsupervised) exploration of different models, ultimately selecting the model at timestep 600,000, which had the highest mean reward. Nevertheless, RL’s approach revealed its susceptibility to excessive oscillations and inconsistencies, necessitating additional optimization and tuning of hyperparameters and reward functions. This conclusion is further substantiated by a range of used performance metrics (objective and subjective), designed to assess the performance of each approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
This study focuses on the analysis of emulsion pictures to understand important parameters. While droplet size is a key parameter in emulsion science, manual procedures have been the traditional approach for its determination. Here we introduced the application of YOLOv7, a recently launched deep-learning model, for classifying emulsion droplets. A comparison was made between the two methods for calculating droplet size distribution. One of the methods, combined with YOLOv7, achieved 97.26% accuracy. These results highlight the potential of sophisticated image-processing techniques, particularly deep learning, in chemistry-related topics. The study anticipates further exploration of deep learning tools in other chemistry-related fields, emphasizing their potential for achieving satisfactory performance.

Abstract
In urban environments, last-mile item delivery relies heavily on trucks, causing issues like noise pollution and traffic congestion. Unmanned Aerial Vehicles (UAVs) offer a promising solution to these challenges. This study compares the effectiveness of delivery using trucks versus drones. Two customer datasets, one clustered and one random, were used for testing. Route optimization involved four deterministic and four non-deterministic algorithms. The performance of these algorithms, considering the total distance traveled, was evaluated across different datasets and vehicle types. The top two algorithms were further assessed for environmental impact and cost efficiency. Battery consumption along the routes was also analyzed to gauge operational feasibility.

Abstract
Cooperative robotics is exponentially gaining strength in scientific research, especially regarding the cooperation between ground mobile robots and Unmanned Aerial Vehicles (UAVs), where the remaining challenges are equipollent to its potential uses in different fields, such as agriculture and electrical tower inspections. Due to the complexity involved in the process, precision landing by UAVs on moving robotic platforms for tasks such as battery hot-swapping is a major open research question. This work explores the feasibility and accuracy of different fiducial markers to aid in the precision landing process by a UAV on a mobile robotic platform. For this purpose, a TelloUAV was used to acquire images at different positions, angles, and distances from ArUco, ARTag, and ArUco Board markers to evaluate their detection precision. The analyses demonstrate the highest reliability in the measurements performed through the ArUco marker. Future work will be devoted to using the ArUco marker to perform precision landing on a mobile robotic platform, considering the necessary adjustments to lessen the impact of errors intrinsic to detecting the fiducial marker during the landing procedure.

Abstract
Autonomous vehicles and robotic manipulators are two examples of mechanically distinct systems. Whether these areas are indoors or outside, the environment in which such vehicles will be employed will play a crucial role in how their locomotion systems develop. The speed and stability of wheeled traditional mobility on ordinary flooring are superior. Leg traction is an efficient method for navigating uneven floors, but it takes more time and uses more energy. The foundation of the hybrid configuration is the creation of a leg that enables the interchange and fusion of the two previously described locomotion methods. One advantage of the hybrid arrangement is that the robot may now be deployed in a wider variety of environments. The goal of this paper is to showcase the creation of a leg for a hybrid locomotive robot. The leg can be printed and constructed at a reasonably low-cost thanks to the design of the numerous 3D modules, which will be made accessible later. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.