Abstract
The development in Multi-Robot 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 collision-free 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.
Highlights
The high versatility and applicability of mobile robots, both industrial and military, has led to an increase in their use over the last decade
It is required to have a prior algorithm for weighing all these conditioning factors and establishing which vehicle is the best to cover a certain task. This algorithm is known as Multi-Robot Task Allocation (MRTA), and it is responsible for assigning the optimal robot–task combination, considering aspects such as the speed of the vehicle, the urgency of the mission, the endurance of the vehicle, the load capacity, or the type of information that can be captured with the onboard sensors
In addition, the idea is to put into operation Unmanned Aerial Vehicles (UAVs) in which the payload is sensors or elements of high economic value, the simulation becomes a crucial element to ensure the proper functioning of all systems
Summary
The high versatility and applicability of mobile robots, both industrial and military, has led to an increase in their use over the last decade. Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have positioned themselves at the forefront of important fields of research oriented to various tasks, such as infrastructure inspection [1,2,3,4], surveillance [5,6], search and rescue [7,8,9], or delivery [10,11] Both vehicles have similarities in terms of their ability to move autonomously or be remotely controlled, they have different characteristics in terms of load capacity, speed, accessibility, or maneuverability. The present work focuses on the exploration and establishment of trajectories considering an environment in 3D, from the mapping and generation of the occupation map of the environment In this way, and through the development of algorithms based on Probabilistic Roadmaps (PRM), a solution is provided for the safe autonomous movement of the different vehicles.
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