Abstract
Nowadays, mobile manipulators are increasing its popularity on modern industries due to their ability to enhance process flexibility and performance. Mobile manipulators are a wide field of research and one of the main directions is trying to control the whole system as a single device. In this context, this paper addresses the problem of path planning of the end-effector of a mobile manipulator. The proposed approach is based on the integration of the kinematic chain of both the manipulator and the omni-directional base. At the end, a collision-free path planner for the mobile manipulator in complex and known environments with obstacles using A * is derived.

Abstract
Path planning for mobile robotics in unknown environments or with moving obstacles requires re-planning paths based on information gathered from the surroundings. Moving obstacles and real time constraints require fast computing to navigate and make decisions in a mobile robot. This paper addresses an optimization approach to compute, with real time constraints, the optimal path for a mobile robot based on a dynamically simplified A* search algorithm with a commitment on the available time.

Abstract
Cable-driven robots have received some attention by the scientific community and, recently, by the industry because they can transport hazardous materials with a high level of safeness which is often required by construction sites. In this context, this research presents an extension of a cable-driven robot called SPIDERobot, that was developed for automated construction of architectural projects. The proposed robot is formed by a rotating claw and a set of four cables, enabling four degrees of freedom. In addition, this paper proposes a new Vision-Guided Path-Planning System (V-GPP) that provides a visual interpretation of the scene: the position of the robot, the target and obstacles location; and optimizes the trajectory of the robot. Moreover, it determines a collision-free trajectory in 3D that takes into account the obstacles and the interaction of the cables with the scene. A set of experiments make possible to validate the contribution of V-GPP to the SPIDERobot while operating in realistic working conditions, as well as, to evaluate the interaction between the V-GPP and the motion controlling system. The results demonstrated that the proposed robot is able to construct architectural structures and to avoid collisions with obstacles in their working environment. The V-GPP system localizes the robot with a precision of 0.006 m, detects the targets and successfully generates a path that takes into account the displacement of cables. Therefore, the results demonstrate that the SPIDERobot can be scaled up to real working conditions.

Abstract
Purpose In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches. Design/methodology/approach A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper. Findings The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems. Originality/value To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

Abstract
Steep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35 degrees). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot's center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.

Abstract
The coordination problem in multi-AGV systems can be approached as an optimization problem and aims to make possible the execution of several tasks simultaneously, avoiding collision and deadlock situations and reducing the average execution time. Time Enhanced A* (TEA*) is one of the path planning algorithms developed for this purpose. This paper focus on the implementation of the TEA* for real industrial applications. In that context, a new approach was developed to complement the TEA* with the capacity to approximate the planning of the future positions for differential robots with its real behaviour.