Abstract
Special care must be taken when considering robots designed to operate collaboratively, such as a swarm, to prevent these agents from being damaged due to unwanted collisions. This work proposes integrating techniques used to move robots, using the Robot Operating System (ROS) and Python's Scikit-Fuzzy module. Thus, this work developed a fuzzy-controlled collaborative obstacle avoidance system for a type of robot whose dynamics are based on motors' vibration. Thus, these robots were designed to participate in a swarm, and the collision must be avoided. In the search for navigation stability, optimal values were sought for the engines' pulse width modulation (PWM).

Abstract
Forests worldwide have been suffering from fires damages, provoking incalculable losses in fauna and flora, economic losses, people and animals' deaths, among other problems. To avoid forest fires catastrophes, it is fundamental to develop innovative operations, such as a forest fire monitoring system. This work concentrates efforts on defining the optimum sensor allocation in a forest fires monitoring system based on a wireless sensor network. Thus, a bi-objective mathematical model is developed to solve the problem, in which the first objective consists of minimising the forest fire hazard of a given forest region, and the second objective refers to the sensors spreading into this region. The developed mathematical model was solved by genetic algorithm and the results demonstrated that the methodology was capable of presenting suitable solutions for the problem. © 2023 American Institute of Physics Inc.. All rights reserved.

Abstract
Forests worldwide have been devastated by fires. Forest fires cause incalculable damage to fauna and flora. In addition, a forest fire can lead to the death of people and financial damage in general, among other problems. To avoid wildfire catastrophes is fundamental to detect fire ignitions in the early stages, which can be achieved by monitoring ignitions through sensors. This work presents an integer programming approach to decide where to locate such sensors to maximize the coverage provided by them, taking into account different types of sensors, fire hazards, and technological and budget constraints. We tested the proposed approach in a real-world forest with around 7500 locations to be covered and about 1500 potential locations for sensors, showing that it allows obtaining optimal solutions in less than 20 min. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Indoor Air Quality (IAQ) pertains to the air quality within a specific space and is directly linked to the well-being and comfort of its occupants. In line with this objective, this research presents a real-time system dedicated to monitoring and predicting IAQ, encompassing both thermal comfort and gas concentration. The system initiates with a data acquisition, wherein a set of sensors captures environmental parameters and transmits this data for storage in a database. The measured parameters are analyzed by a neural network algorithm that predicts anomalies based on historical data. The neural network model generated predictions from 75.9% to 98.1% (depending on the parameter) of precision during regular situations. After that, a test with smoke in the same place was done to validate the model, and the results showed it could detect anomalies. Finally, prediction data are stored in a new database and displayed on a dashboard for monitoring in real-time measured and prediction data. © 2023 IEEE.

Abstract
Although different actions to prevent accidents at work have been implemented in companies, the number of accidents at work continues to be a problem for companies and society. In this way, companies have explored alternative solutions that have improved other business factors, such as predictive analysis, an approach that is relatively new when applied to occupational safety. Nevertheless, most reviewed studies focus on the accident dataset, i.e., the casualty’s characteristics, the accidents’ details, and the resulting consequences. This study aims to predict the occurrence of accidents in the following month through different classification algorithms of Machine Learning, namely, Decision Tree, Random Forest, Gradient Boost Model, K-nearest Neighbor, and Naive Bayes, using only organizational information, such as demographic data, absenteeism rates, action plans, and preventive safety actions. Several forecasting models were developed to achieve the best performance and accuracy of the models, based on algorithms with and without the original datasets, balanced for the minority class and balanced considering the majority class. It was concluded that only with some organizational information about the company can it predict the occurrence of accidents in the month ahead. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Studies dealing with route optimization have received considerable attention in recent years due to the increased demand for transportation services. For decades, scholars have developed robust algorithms designed to solve various Vehicle Routing Problems (VRP). In most cases, the focus is to present an algorithm that can overcome the shortest distances reported in other studies. On the other hand, execution time is also an important parameter that may limit the feasibility of the utilization in real scenarios for some applications. For this reason, in this work, a Guided Local Search (GLS) metaheuristic available in open-source OR-Tools will be tested to solve the Augerat instances of Capacitated Vehicle Routing Problems (CVRP). The stop criterion used here is the execution time, going from 1 s (standard) to 10 s, with a last run of 360 s. The numerical results demonstrate that increasing the execution time returns significant improvement in distance optimization. However, the optimization found considering high execution times can be expensive in terms of time, and not feasible for situations demanding faster algorithms, such as in Dynamic Vehicle Routing Problems (DVRP). Nonetheless, the GLS has proven to be a versatile algorithm for use where distance optimization is the main priority (high execution times) and in cases where faster algorithms are required (low execution times).