Abstract
This survey presents a literature review on the machine and control systems applied in friction stir welding (FSW) with a special focus on the new trends, i.e., using robots and force control. The physical process of FSW is described and the main parameters that are relevant to select a machine and establish a control system are highlighted. The paper reviews in detail the role of the following parameters of a given machine for FSW: force, stiffness, accuracy, sensing, decision-making and flexibility capabilities. They will be compared in terms of the different machines, namely the conventional machine tools, dedicated FSW machines, parallel kinematic robots, and articulated robots. It is stated that articulated arm robots have enormous potentialities in the industrialization of the process, but they also have important limitations namely related to positional accuracy. A quality FSW weld produced by a robot depends on the fine tuning of some process parameters and force/motion control capabilities. Control systems can deal with these limitations. The different approaches to the control systems applied in FSW are presented and their advantages/drawbacks are discussed.

Abstract
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution for the perception task considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to improve the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic dominates the common heuristic (based on the euclidian distance), and present the results of the performance increase in terms of node expansion.

Abstract

Abstract

Abstract
A solution for intuitive robot command and fast robot programming is presented to assemble pins in car doors. Static and dynamic gestures are used to instruct an industrial robot in the execution of the assembly task. An artificial neural network (ANN) was used in the recognition of twelve static gestures and a hidden Markov model (HMM) architecture was used in the recognition of ten dynamic gestures. Results of these two architectures are compared with results displayed by a third architecture based on support vector machine (SVM). Results show recognition rates of 96 % and 94 % for static and dynamic gestures when the ANN and HMM architectures are used, respectively. The SVM architecture presents better results achieving recognition rates of 97 % and 96 % for static and dynamic gestures, respectively.

Abstract