This chapter describes a series of works developed in order to integrate ROS-based robots with Unity-based virtual reality interfaces. The main goal of this integration is to develop immersive monitoring and commanding interfaces, able to improve the operator’s situational awareness without increasing its workload. In order to achieve this, the available technologies and resources are analyzed and multiple ROS packages and Unity assets are applied, such as multimaster_fkie, rosbridge_suite, RosBridgeLib and SteamVR. Moreover, three applications are presented: an interface for monitoring a fleet of drones, another interface for commanding a robot manipulator and an integration of multiple ground and aerial robots. Finally, some experiences and lessons learned, useful for future developments, are reported.

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.

This article presents a new approach to the interception of moving targets in large and complex scenarios. The path planning for interception is based on the Risk-RRT algorithm, which is enhanced by integrating additional information obtained using Fast Marching Method algorithms. Two different techniques based on that method were adapted and integrated within the Risk-RRT, one that obtains the travel distance to the target location and another that estimates the probability of interception at a given point. The proposed approach effectively combines that environmental information with the kinodynamic path planning created by Risk-RRT. The combination of those two algorithms proved to be capable of on-line planning and following an effective interception path, while maintaining the functions of obstacle evasion, handling of uncertainties and reactive navigation.

The current state of technology permits very accurate 3D reconstructions of real scenes acquiring information through quite different sensors altogether. A high precision modelling that allows simulating any element of the environment on virtual interfaces has also been achieved. This paper illustrates a methodology to correctly model a 3D reconstructed scene, with either a camera RGB-D or a laser, and how to integrate and display it in virtual reality environments based on Unity, as well as a comparison between both results. The main interest regarding this line of research consists in the automation of all the process from the map generation to its visualisation with the VR glasses, although this first approach only managed to get results using several programs manually. The long-term objective would be indeed a real-time immersion in Unity interacting with the scene seen by the camera.

This paper presents a study about gait patterns for hexapod robots with extremities called C-legs. The study analyses several modes of gait that different animals use to move through the terrestrial environment, and another new ones that arise when looking at the limitations that present the existing ones. The whole study is reinforced with a series of simulations carried out, where the obtained results are analysed to select the best gait pattern for a specific situation.

This work explores the potential of game theory to solve the task allocation problem in multi-robot missions. The problem considers a swarm with dozens of drones that only know their neighbors, as well as a mission that consists of visiting a series of locations and performing certain activities. Two algorithms have been developed and validated in simulation: one competitive and another cooperative. The first one searches the best Nash equilibrium for each conflict where multiple UAVs compete for multiple tasks. The second one establishes a voting system to translate the individual preferences into a task allocation with social welfare. The results of the simulations show both algorithms work under the limitation of communications and the partial information, but the competitive algorithm generates better allocations than the cooperative one.

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.

Success of teleoperation tasks for mobile robots in disaster scenarios depends largely on the skills of the operator. This article proposes a solution to facilitate this task with two UGVs working together in a master-slave structure. The slave robot is used as an external mobile camera, being able to select the best view for each situation, as you can do in video games. This method has several advantages for overcoming challenging situations that can be found in the mission and it has been tested in the Eurathlon Challenge with good results, completing the tasks in less time and with less stress for operators.

Greenhouse farming is based on the control of the environment of the crops and the supply of water and nutrients to the plants. These activities require the monitoring of the environmental variables at both global and local scale. This paper presents a ground robot platform for measuring the ground properties of the greenhouses. For this purpose, infrared temperature and soil moisture sensors are equipped into an unmanned ground vehicle (UGV). In addition, the navigation strategy is explained including the path planning and following approaches. Finally, all the systems are validated in a field experiment and maps of temperature and humidity are performed.

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.

The productivity of greenhouses highly depends on the environmental conditions of crops, such as temperature and humidity. The control and monitoring might need large sensor networks, and as a consequence, mobile sensory systems might be a more suitable solution. This paper describes the application of a heterogeneous robot team to monitor environmental variables of greenhouses. The multi-robot system includes both ground and aerial vehicles, looking to provide flexibility and improve performance. The multi-robot sensory system measures the temperature, humidity, luminosity and carbon dioxide concentration in the ground and at different heights. Nevertheless, these measurements can be complemented with other ones (e.g., the concentration of various gases or images of crops) without a considerable effort. Additionally, this work addresses some relevant challenges of multi-robot sensory systems, such as the mission planning and task allocation, the guidance, navigation and control of robots in greenhouses and the coordination among ground and aerial vehicles. This work has an eminently practical approach, and therefore, the system has been extensively tested both in simulations and field experiments.

Purpose This paper aims to present a system that is fully capable of addressing the issue of detection, tracking and following pedestrians, which is a very challenging task, especially when it is considered for using in large outdoors infrastructures. Three modules, detection, tracking and following, are integrated and tested over long distances in semi-structured scenarios, where static or dynamic obstacles, including other pedestrians, can be found. Design/methodology/approach The detection is based on the probabilistic fusion of a laser scanner and a camera. The tracking module pairs observations with previously detected targets by using Kalman Filters and a Mahalanobis-distance. The following module allows to safely pursue the target by using a well-defined navigation scheme. Findings The system can track pedestrians from static position to 3.46 m/s (running). It handles occlusions, crossings or miss-detections, keeping track of the position even if the pedestrian is only detected in 55/per cent of the observations. Moreover, it autonomously selects and follows a target at a maximum speed of 1.46 m/s. Originality/value The main novelty of this study is the integration of the three algorithms in a fully operational system, tested in real outdoor scenarios. Furthermore, the addition of labelling to the detection algorithm allows using the full range of a single sensor while preserving the high performance of a combined detection. False-positives rate is reduced by handling the uncertainty level when pairing observations. The inclusion of pedestrian speed in the model speeds up and simplifies tracking process. Finally, the most suitable target is automatically selected by a scoring system.

This paper presents a new approach for the interception of moving objects using UGVs in large complex environments. The planning for interception is based on the Risk-RRT algorithm. Several modifications have been made to the base algorithm to enhance its ability to move in uncertain environments. The planner is integrated with a navigation architecture. The full system is capable of parallel on-line planning and following of the path. It performs the interception and at the same time it avoids static and dynamic obstacles. Several tests, both in simulation and with real world robots, were carried out showing the effectiveness of the proposed system.

Planetary surface exploration is an appealing application of wireless sensor networks that has been investigated in recent years by the space community, including the European Space Agency. The idea is to deploy a number of self-organizing sensor nodes forming a wireless networked architecture to provide a distributed instrument for the study and exploration of a planetary body. To explore this concept, ESA has funded the research project RF Wireless for Planetary Exploration (RF-WIPE), carried out by GMV, SUPSI and UPM. The purpose of RF-WIPE was to simulate and prototype a wireless sensor network in order to assess the potential and limitations of the technology for the purposes of planetary exploration. In this paper, we illustrate the results of the work carried out within the context of RF-WIPE. Two test case scenarios have been investigated: a distributed sensor network-based instrument and networked planetary surface exploration. Each scenario is related to a particular network configuration. For such configurations, energy models and communication protocols have been developed, simulated, and validated both on laboratory tests and with outdoor field tests. Additionally, node deployment was investigated, and a deployment system based on a mobile robotics platform has been designed and tested.

This paper presents a human detection system that can be employed on board a mobile platform for use in autonomous surveillance of large outdoor infrastructures. The prediction is based on the fusion of two detection modules, one for the laser and another for the vision data. In the laser module, a novel feature set that better encapsulates variations due to noise, distance and human pose is proposed. This enhances the generalization of the system, while at the same time, increasing the outdoor performance in comparison with current methods. The vision module uses the combination of the histogram of oriented gradients descriptor and the linear support vector machine classifier. Current approaches use a fixed-size projection to define regions of interest on the image data using the range information from the laser range finder. When applied to small size unmanned ground vehicles, these techniques suffer from misalignment, due to platform vibrations and terrain irregularities. This is effectively addressed in this work by using a novel adaptive projection technique, which is based on a probabilistic formulation of the classifier performance. Finally, a probability calibration step is introduced in order to optimally fuse the information from both modules. Experiments in real world environments demonstrate the robustness of the proposed method.

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.