Abstract
The constant increase in the world population has progressively demanded that humanity develop new technologies to face challenges such as providing high-quality food to the consumer market. In this sense, the concept of precision agriculture arises, proposing the development of agricultural activities such as preparing the land, sowing, planting, treating plants and harvesting automatically through robotic systems. This study focuses on performing a systematic review of the state of the art of robotics applications to execute agricultural activities. Through a comparative analysis of the existing solutions it was possible to highlight the similarities, differences and limitations of several agricultural robots. After looking at the needs of agricultural tasks and the limitations of robots, the challenges that are still unresolved and their possible solutions are indicated. © CLAWAR Association Ltd.

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Correction to: M. F. Silva et al. (Eds.): Robot 2019: Fourth Iberian Robotics Conference, AISC 1092, https://doi.org/10.1007/978-3-030-35990-4 The original version of the book was inadvertently published with incomplete information in the Organization page of the front matter, which has now been included. The book has been updated with the change. © Springer Nature Switzerland AG 2020.

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Motivated by the Fourth Industrial Revolution, there is an ever-increasing need to integrated Cyber-Physical Systems in industrial production environments. To address the demand for flexible robotics in contemporary industrial environments and the necessity to integrate robots and automation equipment in an efficient manner, an effective, bidirectional, reliable and structured data interchange mechanism is required. As an answer to these requirements, this article presents ROBIN, an open-source middleware for achieving interoperability between the Robot Operating System and CODESYS, a softPLC that can run on embedded devices and that supports a variety of fieldbuses and industrial network protocols. The referred middleware was successfully applied and tested in various industrial applications such as battery management systems, motion, robotic manipulator and safety hardware control, and horizontal integration between a mobile manipulator and a conveyor system.

Abstract
Carrying out the task of the exploration of a scene by an autonomous robot entails a set of complex skills, such as the ability to create and update a representation of the scene, the knowledge of the regions of the scene which are yet unexplored, the ability to estimate the most efficient point of view from the perspective of an explorer agent and, finally, the ability to physically move the system to the selected Next Best View (NBV). This paper proposes an autonomous exploration system that makes use of a dual OcTree representation to encode the regions in the scene which are occupied, free, and unknown. The NBV is estimated through a discrete approach that samples and evaluates a set of view hypotheses that are created by a conditioned random process which ensures that the views have some chance of adding novel information to the scene. The algorithm uses ray-casting defined according to the characteristics of the RGB-D sensor, and a mechanism that sorts the voxels to be tested in a way that considerably speeds up the assessment. The sampled view that is estimated to provide the largest amount of novel information is selected, and the system moves to that location, where a new exploration step begins. The exploration session is terminated when there are no more unknown regions in the scene or when those that exist cannot be observed by the system. The experimental setup consisted of a robotic manipulator with an RGB-D sensor assembled on its end-effector, all managed by a Robot Operating System (ROS) based architecture. The manipulator provides movement, while the sensor collects information about the scene. Experimental results span over three test scenarios designed to evaluate the performance of the proposed system. In particular, the exploration performance of the proposed system is compared against that of human subjects. Results show that the proposed approach is able to carry out the exploration of a scene, even when it starts from scratch, building up knowledge as the exploration progresses. Furthermore, in these experiments, the system was able to complete the exploration of the scene in less time when compared to human subjects.