Juan Jesús Roldán

Mini-UAV Based Sensory System for Measuring Environmental Variables in Greenhouses

This paper describes the design, construction and validation of a mobile sensory platform for greenhouse monitoring. The complete system consists of a sensory system on board a small quadrotor (i.e., a four rotor mini-UAV). The goals of this system include taking measures of temperature, humidity, luminosity and CO2 concentration and plotting maps of these variables. These features could potentially allow for climate control, crop monitoring or failure detection (e.g., a break in a plastic cover). The sensors have been selected by considering the climate and plant growth models and the requirements for their integration onboard the quadrotor. The sensors layout and placement have been determined through a study of quadrotor aerodynamics and the influence of the airflows from its rotors. All components of the system have been developed, integrated and tested through a set of field experiments in a real greenhouse. The primary contributions of this paper are the validation of the quadrotor as a platform for measuring environmental variables and the determination of the optimal location of sensors on a quadrotor.

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.

A proposal of methodology for multi-UAV mission modeling

The emergence of multi-UAV missions poses a set of challenges. The control and monitoring of these missions requires to increase the autonomy of fleets and to reduce the workload of operators. The development of an appropriate mission model is fundamental not only for specification and planning but also for control and monitoring. This model allows determining the mission and fleet states and, therefore, providing the operator with adequate information of the mission. This paper poses a methodology to develop multi-UAV mission models and analyzes different modeling techniques, such as Petri nets or hidden Markov models.

Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction

Multi-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences of excessive workload and lack of awareness can vary from inefficiencies to accidents. This work focuses on the study of future operator interfaces of multi-robot systems, taking into account relevant issues such as multimodal interactions, immersive devices, predictive capabilities and adaptive displays. Specifically, four interfaces have been designed and developed: a conventional, a predictive conventional, a virtual reality and a predictive virtual reality interface. The four interfaces have been validated by the performance of twenty-four operators that supervised eight multi-robot missions of fire surveillance and extinguishing. The results of the workload and situational awareness tests show that virtual reality improves the situational awareness without increasing the workload of operators, whereas the effects of predictive components are not significant and depend on their implementation.

A Proposal of Multi-UAV Mission Coordination and Control Architecture

Multi-UAV missions are complex systems that may include a fleet of UAVs, a crew of operators and different computers and interfaces. Currently, an important challenge is the reduction of the number of operators that is required for performing a multi-UAV mission. This challenge can be addressed by increasing the autonomy of fleets and providing capabilities of operators to the interfaces. This paper presents a proposal of control architecture for multi-UAV missions. This architecture shares some elements with centralized and distributed approaches and it has three layers: mission, task and action. The mission layer is implemented in the GCS and performs the mission planning and operator interfacing. Meanwhile, the task and action layers are located in the UAVs and perform respectively the task planning and executing. This architecture is applied to a simulation environment that reproduce a competitive scenario with two fleets of UAVs.

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.

Analyzing and improving multi-robot missions by using process mining

Multi-robot missions can be compared to industrial processes or public services in terms of complexity, agents and interactions. Process mining is an emerging discipline that involves process modeling, analysis and improvement through the information collected by event logs. Currently, this discipline is successfully used to analyze several types of processes, but is hardly applied in the context of robotics. This work proposes a systematic protocol for the application of process mining to analyze and improve multi-robot missions. As an example, this protocol is applied to a scenario of fire surveillance and extinguishing with a fleet of UAVs. The results show the potential of process mining in the analysis of multi-robot missions and the detection of problems such as bottlenecks and inefficiencies. This work opens the way to an extensive use of these techniques in multi-robot missions, allowing the development of future systems for optimizing missions, allocating tasks to robots, detecting anomalies or supporting operator decisions.

Using Process Mining to Model Multi-UAV Missions through the Experience

The interest in civilian missions with multiple unmanned aerial vehicles (UAVs) has increased significantly in recent years, but these missions pose multiple challenges related to operator workload and situational awareness. Human-machine interfaces must consider these challenges and control the amount of information flowing to operators. The authors propose a procedure for automatically modeling multi-UAV missions that uses process mining to discover Petri nets through event logs. Specifically, it applies several discovery algorithms, generates and evaluates models, and determines the best among the four.

Determining mission evolution through UAV telemetry by using decision trees

The control and monitoring of the UAV missions is a challenge in terms of operator workload. Two relevant issues are the situational awareness and the decision support of operators. This paper proposes a system that is able to analyze the telemetry of UAV to deduce the state of mission. This system uses Petri nets for determining the state and decision trees for estimating the evolution. Both the Petri nets and the decision trees are generated automatically from the telemetry of previous missions. The whole system is validated by monitoring a set of UAV missions in a realistic simulator. The results can be applied to diverse areas, including the development of intelligent and adaptive interfaces or decision support systems.

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.