This paper discusses a novel collective sensing approach using autonomous sensors specially designed to monitor gas leaks and search for gas sources. The proposed collective behavior aims to improve the gas-source search by sharing information between mobile sensors and reducing the risks associated with gas leakage. The group acts as a composite sensor that can move independently to search for an optimal sensing zone. The autonomous searching behavior is bio-inspired by colonies of bacteria that continuously seek energy sources throughout their existence. Each sensor makes its own autonomous search decision, considering the group sense, to move in the direction of a better energy source. The collective approach is based on autonomous agents sharing information to achieve a collective sense of gas perception and utilizes more intelligent searching. The method is evaluated in a cyber-physical system specially developed to safely experiment with gases and mobile sensors while reproducing the realistic dynamic behavior of the gas. Experiments are performed to clarify the collective gas-sensing contributions, and the gas search is compared through multiple mobile sensors with and without collective sensing. The proposed approach is evaluated in an unhealthy environment to elucidate its effectiveness. In addition to presenting the related differences between collective and individual sensory approaches, this work contributes with analyzes of the scalability of mobile gas sensing systems. This work also contributed as a simulated semi-physical experimental system to test algorithms' performance before applying it to practice.

This study presents an innovative technique for the predictive inspection of power lines utilizing an autonomous line-crawling inspection robot equipped with multimodal sensors. The system enhances the robot’s capabilities by integrating comprehensive analyses of power line conditions and correlating these assessments with historical data to predict the future state of power infrastructure. The inspection framework is organized into three modules: a frontal multimodal sensor module for examining key power components, such as dampers, wire markers, and insulators; a cable inspection module for detailed analysis of power cables; and a perpendicular perception module to evaluate power elements in the lower plane and monitor vegetation encroachment. The results demonstrate a significant enhancement in the robot’s ability to accurately identify and categorize power line components, leveraging the advantages of various techniques. The system generates composite inspection data that offers a comprehensive assessment of the power line condition in a consolidated multimodal inspection map by standardizing and correlating information collected from multiple viewpoints. This synthesis of diverse sensor data enables early detection of issues and supports proactive maintenance strategies. The proposed method represents a significant advancement in power line inspection, providing enhanced reliability, safety, and operational efficiency.

The use of collaborative robot systems to perform specific tasks is a strong research area on robotic systems. Robot platforms are becoming cheaper, increasing the number of applications and new tasks variations on research labs. A collaborative system that join together small mobile robots to scan some specific area and a drone to set their position on the ground is a good example of the possible applications of this kind. This work proposes the use of an autonomous quadri-copter and “dummy” small ground robots on a collaborative task that uses virtual reality tags to estimate robot positions and orientations on a simulation scene and provide information for their control. The robots are “dummy” because do not have any embedded odometer. The position and orientation data are achieved by the drone and transmitted via ROS to a computational system that runs a PID control for each individual robot. The simulation runs on a V-REP scene and use a C++ code to control the mobile robots moves.

This article discusses a novel approach to collective mapping that uses autonomous sensors to create safe routes in environments with gas leaks. Multiple mobile sensors seek to delimit different regions and assign them different tasks according to their momentary needs. In this study, robot groups act as multiple composite sensors that can move independently according to their assigned functions. Autonomous detection, collective mapping, and collective decision-making of the robots are behaviors bioinspired by the cognitive mechanisms observed in bacterial colonies. These colonies continuously seek to maintain the lives of their species based on their collective decision-making in searching for energy sources and changing colony size. In addition, as gas dispersion in the environment increases, the received data from the sensors aids collective decision-making, assigning different functions, such as mapping, environment exploration, and route creation, to groups of mobile sensors. Depending on the momentary need, the number of sensors in each group changes. The proposed method in this study was based on real mobile robots with characteristics that enable varying levels of scalability in size. Subsequently, it was evaluated in a simulated system and developed for experimentation with gas and mobile sensors in a dynamic and realistic environment. This study further contributes to analyzing multiple tasks in homogeneous sensor groups executing different tasks. Furthermore, this work introduced a simulated experimental system to test different topologies of multiple scalable mobile sensors.