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.

Often inspection and maintenance work involve a large number of highly dangerous manual operations, especially within industrial fields such as shipbuilding and construction. This paper deals with the autonomous climbing robot which uses the “caterpillar” concept to climb in complex 3D metallic-based structures. During its motion the robot generates in real-time the path and grasp planning in order to ensure stable self-support to avoid the environment obstacles, and to optimise the robot consumption during the inspection. The control and monitoring of the robot is achieved through an advanced Graphical User Interface to allow an effective and user friendly operation of the robot. The experiments confirm its advantages in executing the inspection operations.

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

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a time-changing fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison.