One of the active challenges in multi-robot missions is related to managing operator workload and situational awareness. Currently, the operators are trained to use interfaces, but in the near future this can be turned inside out: the interfaces will adapt to operators so as to facilitate their tasks. To this end, the interfaces should manage models of operators and adapt the information to their states and preferences. This work proposes a videogame-based approach to classify operator behavior and predict their actions in order to improve teleoperated multi-robot missions. First, groups of operators are generated according to their strategies by means of clustering algorithms. Second, the operators' strategies are predicted, taking into account their models. Multiple information sources and modeling methods are used to determine the approach that maximizes the mission goal. The results demonstrate that predictions based on previous data from single operators increase the probability of success in teleoperated multi-robot missions by 19%, whereas predictions based on operator clusters increase this probability of success by 28%.@en

Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life-long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual cues, follow it, and robustly land on it. The system relies on a finite state machine, which together with a novel re-localization module allows the system to operate robustly for extended periods of time and to recover from potential failed landing maneuvers. Two approaches for tracking and landing are developed, implemented, and tested. The first variant is based on a novel height-adaptive PID controller that uses the current position of the landing platform as the target. The second one combines this height-adaptive PID controller with a Kalman filter in order to predict the future positions of the platform and provide them as input to the PID controller. This facilitates tracking and, mainly, landing. Both the system as a whole and the re-localization module in particular have been tested extensively in a simulated environment (Gazebo). We also present a qualitative evaluation of the system on the real robotic platforms, demonstrating that our system can also be deployed on real robotic platforms. For the benefit of the community, we make our software open source.@en

This paper presents a novel tool capable of collecting thermal signatures inside a building by using low-cost IR temperature sensors mounted on-board an aerial platform. The proposed system aims to facilitate the detection of heat loss inside buildings, which is a key aspect for improving energy efficiency in large commercial or industrial buildings. Current detection systems usually require manual labor as well as the use of expensive instrumentation. The proposed system on the other hand, relies on the use of a small unmanned aerial vehicle carrying low-cost thermopile IR sensors. Moreover, the system delivers a fast temperature sensing scheme and it provides coverage to inaccessible areas, thus overcoming the limitations of current mobile platforms which use ground robots. Different experiments were carried out in order to assess the behavior of the sensors as well as to validate the full system. Moreover, the hypothesis that thermopile IR sensors can be used to track temperature signature on-the-fly is validated experimentally with the use of the proposed system over different targets.@en

This chapter presents a series of experiences and lessons learned during several implementations and real-world tests of ROS-based Multi-Robot Systems. It also describes, analyses and compares several ROS components relevant for these applications, taking into account the scenarios where they can be used. Also, some general issues of importance of Multi-Robot Systems on real-world, such as software and communications architectures, types of information shared are described in detail. Finally, the difficulties and specific challenges that arose when using a Multi-Robot Systems for any application will be discussed.

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This work presents a complete multirobot solution for signal searching tasks in large outdoor scenarios. An evaluation of two different coverage path-planning strategies according to field size and shape is presented. A signal location system developed to simulate mines or chemical source detections is also described. The solution presented is a pioneer in evaluating multimaster robotics operative system architectures with a fleet of robots in real scenarios. This solution minimizes the use of communications bandwidth required for full operation. Finally, field results are provided, and the advantages of the implemented solution are analyzed.@en

There are many outdoor robotic applications where a robot must reach a goal position or explore an area without previous knowledge of the environment around it. Additionally, other applications (like path planning) require the use of known maps or previous information of the environment. This work presents a system composed by a terrestrial and an aerial robot that cooperate and share sensor information in order to address those requirements. The ground robot is able to navigate in an unknown large environment aided by visual feedback from a camera on board the aerial robot. At the same time, the obstacles are mapped in real-time by putting together the information from the camera and the positioning system of the ground robot. A set of experiments were carried out with the purpose of verifying the system applicability. The experiments were performed in a simulation environment and outdoor with a medium-sized ground robot and a mini quad-rotor. The proposed robotic system shows outstanding results in simultaneous navigation and mapping applications in large outdoor environments.

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Aerial multi-robot systems are a robust remote sensing choice to collect environmental data from the Earth's surface. To accomplish this mission in a collaborative way, unmanned aerial vehicles must perform a full coverage trajectory over a target area while acquiring imagery of it. In this paper we address the multi coverage path planning problem with an aerial vehicles team. The approach proposed is hybrid, since is it is composed by an on-line and an off-line steps. This work is based on an optimal solution which is discretized to compute the coverage paths. This work proposes a multi coverage path planning solution making use of computer graphics tools in the world transformation from continuous to discrete, focusing on the aerial images acquisition. The workspace transformation from continuous to discrete is discussed and raster graphics based algorithms are employed.@en