This paper describes the control of an automatic overhead crane for assembly of modular building elements. The automatic system was developed using a commercial full-size crane, which was modified by adding adequate sensors, servomotors, and control strategy. The crane, which maintained its original cables, was converted to a robotic system, and is controlled via a computer-based multiaxes control board. The implemented algorithms solve two main problems of module assembly: 1) precision positioning of large and heavy modules in the order of a few centimeters, and 2) anti-swinging transportation of modules, even in difficult weather conditions (not extreme). The implementation of the anti-swinging control is achieved using the on-line two-dimensional inclinometer measurements and an on-line calculation of input impulse trains. The developed input-shaping control is divided into two phases: straight line motion and external perturbances cancellation. Experimental results of the developed algorithms which demonstrate the effectiveness of the new process are presented.

This paper presents a new sensor based global path planner which operates in two steps. In the first step the safest areas in the environment are extracted by means of a Voronoi diagram. In the second step fast marching method is applied to the Voronoi extracted areas in order to obtain the shortest path. In this way the trajectory obtained is the shortest between the safe possible ones. This two step method combines an extremely fast global planner operating on a simple sensor based environment modeling, while it operates at the sensor frequency. The main characteristics are speed and reliability, because the map dimensions are reduced to a unidimensional map and this map represents the safest areas in the environment for moving the robot

This article provides a comprehensive view of the novel fast marching (FM) methods we developed for robot path planning. We recall some of the methods developed in recent years and present two improvements upon them: the saturated FM square (FM²) and an heuristic optimization called the FM² star (FM^2*) method.

Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.