Multirobot Online Sampling Scheduler for Persistent Monitoring The employment of autonomous agents for persistent monitoring tasks has significantly increased in recent years. In this sense, the data collection process must take into account limited resources, such as time and energy, whilst acquiring a sufficient amount of data to generate accurate models of underlying phenomena. Many different schedulers in the literature act in an offline manner, which means they define the sequence of visit and generate a set of paths before any observations are made. However, online approaches can adapt their behavior based on previously collected data, allowing to obtain more precise models. In this paper, we propose an online scheduler which evaluates the sampling rate of the signals being measured to assign different priorities to different PointsofInterest (PoIs). Next, according to this priority, it is determined if a region must be visited more or less frequently to increase our knowledge of the phenomenon. Our methodology was evaluated through several experiments in a simulated environment.

SwarMap: Occupancy Grid Mapping with a Robotic Swarm In recent years, robotic swarms have been studied as an alternative to replace complex and expensive robots in the execution of different types of tasks. In this paper, we propose an approach to cooperatively build occupancy grid maps using large groups of simpler robots. Robots randomly explore the environment and update a shared occupancy grid stored in the cloud using Bayesian Filters. They localize themselves based on odometry and use pairtopair neighborhood communication to exchange pose information and reduce the uncertainty through a Kalman Filter. Simulated and real experiments are performed to show the effectiveness and scalability of the proposed approach.