3D Trajectory Planning Method for UAVs Swarm in Building Emergencies The development in MultiRobot Systems (MRS) has become one of the most exploited fields of research in robotics in recent years. This is due to the robustness and versatility they present to effectively undertake a set of tasks autonomously. One of the essential elements for several vehicles, in this case, Unmanned Aerial Vehicles (UAVs), to perform tasks autonomously and cooperatively is trajectory planning, which is necessary to guarantee the safe and collisionfree movement of the different vehicles. This document includes the planning of multiple trajectories for a swarm of UAVs based on 3D Probabilistic Roadmaps (PRM). This swarm is capable of reaching different locations of interest in different cases (labeled and unlabeled), supporting of an Emergency Response Team (ERT) in emergencies in urban environments. In addition, an architecture based on Robot Operating System (ROS) is presented to allow the simulation and integration of the methods developed in a UAV swarm. This architecture allows the communications with the MavLink protocol and control via the Pixhawk autopilot, for a quick and easy implementation in real UAVs. The proposed method was validated by experiments simulating building emergences. Finally, the obtained results show that methods based on probability roadmaps create effective solutions in terms of calculation time in the case of scalable systems in different situations along with their integration into a versatile framework such as ROS.

Multi Robot Route Planning (MRRP): Extended SpatialTemporal Prioritized Planning Autonomous vehicles are, in contrast to classical automated guided vehicles (AGVs), less predictable in their behavior and drive time. Therefore, the issue of how to efficiently control these vehicles arises, because autonomous agents need to be coordinated and not controlled, to give autonomous behaviors and actions space. The scientific contribution of this paper is a novel approach, based on prioritized planning to target this issue as well as an open source framework for evaluation and comparison. Prioritized planning has the disadvantage of being neither optimal nor complete, however, it has the advantage of being computationally feasible. This work utilizes prioritized planning to significantly increase the set of feasible scenarios through collision prevention: by locally finding alternative routes and adding them to the search graph. The paper clearly formulates the extensions needed and delineates the approachxe2x80x99s limits, as it is neither optimal nor complete. More importantly, however, our method calculates routes for each vehicle with intervehicle synchronization, enabling vehicles to execute the plan in a distributed fashion without centralized control, thereby allowing autonomous behavior. Finally, results are verified by comparing our Multi Robot Router (MRR) proposed in this work to classical approaches. The software developed as well as the test sets are publicly available for ROS and the simulation environment.