Abstract
When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.

Abstract
Over the last decades, Serious Games have gained increased importance, mainly due to the evolution and expansion of video games and its application in multiple areas. Integration in the world of sport is one of the solutions that individuals with disabilities or motor disorders develop to feel more socially integrated, more independent and confident with themselves. Boccia is a Paraly-mpic sport that is increasingly getting more attention around the world. This has contributed for the objectives of this project since it attracts these patients a lot more easily and including it in the Serious Games category enables them to develop and rehabilitate their cognitive capabilities. It will allow the users being dynamic, holding their attention and motivating them instead of the traditional cognitive rehabilitation processes that quickly become repetitive and discouraging. This paper describes a realistic Boccia game simulator adapted for people with disabilities or motor disorders still on development that aims to integrate a set of features that include real physics, multimodal user interface and social features (diversion, rehabilitation, competition and improvement). These features can be used to enhance the interest of non-practitioners of the sport and to improve the training conditions of Boccia athletes. Results observed in an experiment with real Boccia game components indicate that the simulator offers great similarity to the reality with the maximum difference between the measures obtained in both being 10 cm.

Abstract
This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory which is a new robotic competition presented in Lisbon 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that is well-behaved. The sensor information is continuously updated in time and space through the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, a particle filter based on Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high. Meaning that the map-matching is unreliable and robot is lost. The experiments conducted in this paper prove the ability and accuracy of the presented technique to localize small mobile robots for this competition. Therefore, extensive results show that the proposed method have an interesting localization capability for robots equipped with a limited amount of sensors.

Abstract
This paper presents a set of novel modifications that can be applied to any grid-based path planning algorithm from the A* family used in mobile robotics. Five modifications are presented regarding the way the robot sees an obstacle and its target to plan the robot's path. The modifications make it possible for the robot to get to the target faster than traditional algorithms, as well as to avoid obstacles that move as fast as (or even faster than) the robot. Some simulations were made using a crowded and highly dynamic environment with twelve randomly moving obstacles. In these first simulations, a middle sized 5DPO robot was used. Also, real experiments were made with a small-sized version of a 5DPO robot to validate the algorithm's effectiveness. In all simulations and real robot experiments the objects are considered to be moving at a constant speed. Finally, we present an overall discussion and conclusion of this paper. © 2012 The Brazilian Computer Society.

Abstract

Abstract
This paper aims to develop grasping and manipulation capability along with autonomous navigation and localization in a wheelchair-mounted robotic arm to serve patients. Since the human daily environment is dynamically varied, it is not possible to enable the robot to know all the objects that would be grasped. We present an approach to enable the robot to detect, grasp and manipulate unknown objects. We propose an approach to construct the local reference frame that can estimate the object pose for detecting the grasp pose of an object. The main objective of this paper is to present the grasping and manipulation approach along with a navigating and localization method that can be performed in the human daily environment. A grid map and a match algorithm is used to enable the wheelchair to localize itself using a low-power computer. The experimental results show that the robot can manipulate multiple objects and can localize itself with great accuracy.