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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.

Abstract
Industrial robots are a technology which is highly present in industry and can perform several tasks, namely machining activities. Different than CNC machines, which work with G-code and have available several software applications to generate the machine code, there is a lack of software for robotic arms, in addition to each application depending on its own language and software. This work studied a way to use different robotic arms for 3D part machining processes, to perform 3D objects reconstruction obtained through a low-cost 3D scanner. Dealing with the 3D reconstruction by integrating 3D acquisition and robotic milling with software available on the market, this paper presents a system that acquires and reconstructs a 3D object, in order to seek greater flexibility with lower initial investments and checking the applicability of robot arm in these tasks. For this, a 3D object is scanned and imported to a CAD/CAM software, to generate the machining toolpath, and a software application is used to convert the G-code into robot code. Several experiments were performed, using an ABB IRB 2600 robot arm, and the results of the machining process allowed to validate the G-code conversion and milling process using robotic arms, according to the proposed methodology.

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The transportation of large loads is still an obstacle to the productivity of some companies. To answer this internal logistics challenge, one solution with high potential is the use of several mobile robots that can execute these tasks cooperatively. This paper addresses the simulation of strategies that allow two omnidirectional robots, a leader and a follower, to coordinate their movement and cooperate in a transportation task (co-transport). This is achieved by implementing a force sensor on the mechanical structure of a support that allows the follower to grab and transport the load on the top of its chassis. Through the measurement of the forces, this robot will be able to react to the movement of the leader, whose function will be to guide the system to its final destination. The system and its cooperation strategies were idealized and then tested in the CoppeliaSim simulator. The results obtained through simulation were the target of an analysis that assessed which strategy best complemented the omnidirectional mobility of the system's constituents and, at the same time, ensured that less forces were exerted on the load during transport.

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