Abstract
In the past few years, cable-driven robots have received some attention by the scientific community and the industry. They have special characteristics that made them very reliable to operate with the level of safeness that is required by different environments, such as, handling of hazardous materials in construction sites. This paper presents a cable-driven robot called SPIDERobot, that was developed for automated construction of architectural projects. This robot has a rotating claw and it is controlled by a set of 4 cables that allow 4 degrees of freedom. In addition to the robot, this paper introduces a Dynamic Control System (DCS) that controls the positioning of the robot and assures that the length of cables is always within a safe value. Results show that traditional force-feasible approaches are more influenced by the pulling forces or the geometric arrangement of all cables and their positioning is significantly less accurate than the DCS. Therefore, the architecture of the SPIDERobot is designed to enable an easily scaling up of the solution to higher dimensions for operating in realistic environments.

Abstract
The Robot@Factory competition was recently included in Robotica, the main Portuguese Robotics Competition. This robot competition takes place in an emulated factory plant, where automatic guided vehicles (AGVs) must cooperate to perform tasks. To accomplish their goals, the AGVs must deal with localization, navigation, scheduling, and cooperation problems that must be solved autonomously. This robot competition can play an important role in education due to its inherent multidisciplinary approach, which can motivate students to bridge different technological areas. It can also play an important role in research and development, because it is expected that its outcomes will later be transferred to real-world problems in manufacturing or service robots. By presenting a scaled-down factory shop floor, this competition creates a benchmark that can be used to compare different approaches to the challenges that arise in this kind of environment. The ability to alter the environment, in some restricted areas, can usually promote the test and evaluation of different localization mechanisms, which is not possible in other competitions. This paper presents one of the possible approaches to build a robot capable of entering this competition. It can be used as a reference to current and new teams.

Abstract
Purpose - Streamlining automated processes is currently undertaken by developing optimization methods and algorithms for robotic manipulators. This paper aims to present a new approach to improve streamlining of automatic processes. This new approach allows for multiple robotic manipulators commonly found in the industrial environment to handle different scenarios, thus providing a high-flexibility solution to automated processes. Design/methodology/approach - The developed system is based on a spatial discretization methodology capable of describing the surrounding environment of the robot, followed by a novel path-planning algorithm. Gazebo was the simulation engine chosen, and the robotic manipulator used was the Universal Robot 5 (UR5). The proposed system was tested using the premises of two robotic challenges: EuRoC and Amazon Picking Challenge. Findings - The developed system was able to identify and describe the influence of each joint in the Cartesian space, and it was possible to control multiple robotic manipulators safely regardless of any obstacles in a given scene. Practical implications - This new system was tested in both real and simulated environments, and data collected showed that this new system performed well in real- life scenarios, such as EuRoC and Amazon Picking Challenge. Originality/ value - The new proposed approach can be valuable in the robotics field with applications in various industrial scenarios, as it provides a flexible solution for multiple robotic manipulator path and motion planning.

Abstract
In this paper the authors focus on presenting a new path planning approach for a multi-robot transportation system in an industrial case scenario. The proposed method is based on the A* heuristic search in a cell decomposition scenario, for which a time component was added - Time Enhanced A* or simply TEA*. To access the flexibility and efficiency of the proposed algorithm, a set of experiments were performed in a simulated industrial environment. During trials execution the proposed algorithm has shown high capability on preventing/dealing with the occurrence of deadlocks in the transportation system.

Abstract
This paper presents an algorithm capable of generating smooth, feasible paths for an any-shape non-holonomic mobile robot, taking into account orientation restrictions, with the aim of navigating close to obstacles. Our contribution consists in an extension of the A* algorithm in a cell decomposition, where besides its position, the orientation of the platform is also considered when searching for a path. This is achieved by constructing 16 layers of orientations and only visiting neighbor layers when searching for the lowest cost. To simplify collision checking, the robot's footprint is used to inflate obstacles, yet, to allow the robot to find paths close to obstacles, the actual footprint of the robot must used. By discretizing the orientation space into layers and computing an oriented footprint for each layer, the actual footprint of the robot is used, increasing the configuration space without becoming computationally expensive. The path planning algorithm was developed under the EU-funded project CARLoS(1) and was implemented in a stud welding robot simulated within a naval industry environment, validating our approach.

Abstract
Traffic Control is one of the fundamental problems in the management of an Automated Guided Vehicle (AGV) system. Its main objectives are to assure efficient conflict free routes and to avoid/solve system deadlocks. In this sense, and as an extension of our previouswork, this paper focus on exploring the capabilities of the Time Enhanced A* (TEA*) to dynamically control a fleet of AGVs, responsible for the execution of a predetermined set of tasks, considering an automatic warehouse case scenario. During the trial execution the proposed algorithm, besides having shown high capability on preventing/dealing with the occurrence of deadlocks, it also has exhibited high efficiency in the generation of free collision trajectories. Moreover, it was also selected an alternative from the state-of-art, in order to validate the TEA* results and compare it.