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 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.

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 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.

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.