Abstract
There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The presented paper addresses a comparison between two optimization methods, the Stretched Simulated Annealing and the Genetic Algorithm, that runs in an accurate and stable simulation model. Final results show the comparative study and demonstrate that optimization is a valid gait planning technique.

Abstract
A laser scanner is a popular sensor widely used in industry and mobile robots applications that measures the distance to the sensor on a slice of the plan. At the same time, simulation has becoming more and more used in industries and academia since it presents several advantages. It takes the building and rebuilding phase out of the loop by using the model already created in the design phase. Further, simulation time on testing is cheaper and faster than performing the multiple tests of the design each time. Besides, it is easier to measure some variables in simulation than in real scenarios. In this paper, a laser scanner sensor is modeled and implemented in a developed simulator that already has several other sensors and actuators models. The presented simulation reflects the laser model properties such as target color dependences, noise, limits, time constraints, and target angle functions. As a case study, the same scenario is assembled with real components on a conveyer belt and in simulation. Results from both approaches are compared and validate the proposed model methodology. As an example, a 3D object recognition task is addressed highlighting the developed realistic model. Further industrial and R&D implementations based on this sensor could be stressed in simulation before implementation. © Springer International Publishing Switzerland 2013.

Abstract
This paper describes the EMG30 mechanical and electrical modeling and its simulation resorting to SimTwo (Robot@Factory mobile robot competition official simulator). It is described the developed setup applied to obtain the experimental data that was used to estimate the actuator parameters. It was obtained an electro-mechanical dynamical model that describes the motor, its gear box, and the encoder. The motivation to model and simulate the EMG30 is the fact that it is an actuator worldwide popular in the mobile robotics domain, being a low cost 12v motor equipped with encoders and a 30:1 reduction gearbox. The Goal of this work is to provide more realism and new features to the Robot@Factory official simulator, allowing participating teams to produce and validate different robot prototypes and its software, reducing considerably the development time. © Springer International Publishing Switzerland 2013.

Abstract
Industrial laser cutting machines use a type of support base that sometimes causes the cut metal parts to tilt or fall, which hinders the robot from picking the parts after cutting. The objective of this work is to calculate the 3D orientation of these metal parts with relation to the main metal sheet to successfully perform the subsequent robotic pick-and-place operation. For the perception part the system relies on the low cost 3D sensing Microsoft Kinect, which is responsible for mapping the environment. The previously known part positions are mapped in the new environment and then a plane fitting algorithm is applied to obtain its 3D orientation. The implemented algorithm is able to detect if the piece has fallen or not. If not, the algorithm calculates the orientation of each piece separately. This information is later used for the robot manipulator to perform the pick-and-place operation with the correct tool orientation. This makes it possible to automate a manufacturing process that is entirely human dependent nowadays.

Abstract

Abstract
Cooperation with humans is a requirement for the next generation of robots so it is necessary to model how robots can sense, know, share and acquire knowledge from human interaction. Instead of traditional SLAM (Simultaneous Localization and Mapping) methods, which do not interpret sensor information other than at the geometric level, these capabilities require an environment map representation similar to the human representation. Topological maps are one option to translate these geometric maps into a more abstract representation of the the world and to make the robot knowledge closer to the human perception. In this paper is presented a novel approach to translate 3D grid map into a topological map. This approach was optimized to obtain similar results to those obtained when the task is performed by a human. Also, a novel feature of this approach is the augmentation of topological map with features such as walls and doors.