Abstract
The use of robotic palletizing systems has been increasing in the so-called Fast Moving Consumer Goods (FMCG) industry. However, because of the type of solutions developed, focused on high performance and efficiency, the degree of adaptability of packaging solutions from one type of product to another is extremely low. This is a relevant problem, since companies are changing their production processes from low variety/high volume to high variety/low volume. This environment requires companies to perform the setup of their robots more frequently, which has been leading to the need to use offline programming tools that decrease the required robot stop time. This work addresses these problems and, in this paper, is described the solution proposed for the automated offline development of collision free robot programs for palletizing applications. © Springer International Publishing AG 2018.

Abstract
Object tracking in a moving frame is becoming a common requirement in a lot of mobile robotic applications, such as search and rescue, monitoring and surveillance, and even in some scientific applications, such as robotic soccer. In all these applications, the robots must be capable of estimating the target position and, sometimes, velocity on their own. Depending on the application and on the current scene situation, the estimates must be more or less accurate, depending on the robot intention to interact with the target, whether to catch it, follow it, etc. The problem is that a robot moves along the working area, having some uncertainty in its pose estimation. This paper proposes an approach based on a Kalman Filter to estimate the object position and velocity, regardless the robot pose. As a testbed, a Middle-Size League soccer robot tracking a moving ball example will be used. This approach allows the agent to follow and interact with a moving object, even if its localization is not available. © Springer International Publishing AG 2018.