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.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+ D en la Comunidad de Madrid and co-funded by Structural Funds of the EU, and from the DPI2014-56985-Rproject (Protección robotizada de infraestructuras críticas) funded by the Ministerio de Economía y Competitividad of Gobierno de España. This work is framed on the SAVIER (Situational Awareness Virtual EnviRonment) Project, which is both supported and funded by Airbus Defence & Space. The experiments were performed in an educational greenhouse of the E.T.S.I.Agrónomos of Technical University of Madrid.Peer Reviewe

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Combining multiple Large Volume Metrology systems: Competitive versus cooperative data fusion

Large Volume Metrology (LVM) tasks can require the concurrent use of several measuring systems. These systems generally consist of set of sensors measuring the distances and/or angles with respect to a point of interest so as to determine its 3D position. When combining different measuring systems, characterized by sensors of different nature, competitive or cooperative methods can be adopted for fusing data. Competitive methods, which are by far the most diffused in LVM, basically perform a weighted mean of the 3D positions determined by the individual measuring systems. On the other hand, for cooperative methods, distance and/or angular measurements by sensors of different systems are combined together in order to determine a unique 3D position of the point of interest. This paper proposes a novel cooperative approach which takes account of the measurement uncertainty in distance and angular measurements of sensors of different nature. The proposed approach is compared with classical competitive approaches from the viewpoint of the metrological performance. The main advantages of the cooperative approach, with respect to the competitive one, are: (i) it is the only option when the individual LVM systems are not able to provide autonomous position measurements (e.g., laser interferometers or single cameras), (ii) it is the only option when only some of the sensors of autonomous systems work correctly (for instance, a laser tracker in which only distance – not angular – measurements are performed), (iii) when using systems with redundant sensors (i.e. photogrammetric systems with a large number of distributed cameras), point localization tends to be better than that using the competitive fusion approach

A one decade survey of autonomous mobile robot systems

Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability