Mario Garzón

An Aerial–Ground Robotic System for Navigation and Obstacle Mapping in Large Outdoor Areas

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.

Human Detection from a Mobile Robot Using Fusion of Laser and Vision Information

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.

Heterogeneous Multi-Robot System for Mapping Environmental Variables of Greenhouses.

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.

Multi-robot visual coverage path planning: geometrical metamorphosis of the workspace through raster graphics based approaches

Aerial multi-robot systems are a robust remote sensing choice to collect environmental data from the Earths 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.

A UGV Approach to Measure the Ground Properties of Greenhouses

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.

RiskRRT-Based Planning For Interception of Moving Objects in Complex Environments

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.

Pedestrian Trajectory Prediction in Large Infrastructures

This paper presents a pedestrian trajectory prediction technique. Its mail novelty is that it does not require any previous observation or knowledge of pedestrian trajectories, thus making it useful for autonomous surveillance applications. The prediction requires only a set of possible goals, a map of the scenario and the initial position of the pedestrian. Then, it uses two different path planing algorithms to find the possible routes and transforms the similarity between observed and planned routes into probabilities. Finally, it applies a motion model to obtain a time-stamped predicted trajectory. The system has been used in combination with a pedestrian detection and tracking system for real-world tests as well as a simulation software for a large number of executions.

Multi-robot Systems, Virtual Reality and ROS: Developing a New Generation of Operator Interfaces

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.

Using ROS in Multi-robot Systems: Experiences and Lessons Learned from Real-World Field Tests

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.

A Game of Drones: Game Theoretic Approaches for Multi-robot Task Allocation in Security Missions

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.