Abstract
This paper presents the evaluation of a serious game project, where the primary goal was to develop a set of collaborative game levels on a virtual campus, in order to help the integration process of newcomer students to the university. The global activities that can be performed by the students were designed for a group approach in a controlled virtual environment. For the present work we have selected Second Life for the implementation of these collaborative "game levels". A prototype evaluation was conducted to collect results with a sample of university students. With this data, some conclusions were extracted in order to delineate future developments.

Abstract
The main objective of this work is to describe the Virtual Center for the Rehabilitation of Road Accident Victims - VICERAVI and the serious games it includes for the purpose of neuropsychological rehabilitation. The validation of the VICERAVI as a rehabilitation system involves 30 participants of both sexes, with brain injury due to road accidents. The applicability and the benefits of virtual worlds for a holistic neuropsychological rehabilitation will be tested using virtual reality (OpenSimulator) to simulate tasks of everyday life,,enabling participants to perform cognitive and social skills training through their avatars. The progresses of this group will se compared with a group of conventional neuropsychological rehabilitation. The main innovation of the work is the possibility if administering neuropsychological rehabilitation at distance, via ecological virtual environments. Moreover, the paper discusses also relevant criteria and issues in respect to the use of serious games, simulation and virtual environments in rehabilitation.

Abstract
The development platform of a digital game is frequently defined before any game design document. Without a proper examination, this could exclude other viable solutions. Although the online virtual world Second Life is sometimes erroneously denominated a game, it can be used as a development platform for many purposes. This paper analyses game creation within the Second Life virtual world and possible approaches for the implementation of game assets and game logic. There is also an analysis of what game types could be more suited for development under this platform. Experiences learned from the developed prototype are shown and hints are given as to evaluate different techniques and expose best practices. © 2011 AISTI.

Abstract
The estimation of a 3D sensor constellation for maximizing the observable surface area percentage of a given set of target objects is a challenging and combinatorial explosive problem that has a wide range of applications for perception tasks that may require gathering sensor information from multiple views due to environment occlusions. To tackle this problem, the Gazebo simulator was configured for accurately modeling 8 types of depth cameras with different hardware characteristics, such as image resolution, field of view, range of measurements and acquisition rate. Later on, several populations of depth sensors were deployed within 4 different testing environments targeting object recognition and bin picking applications with increasing level of occlusions and geometry complexity. The sensor populations were either uniformly or randomly inserted on a set of regions of interest in which useful sensor data could be retrieved and in which the real sensors could be installed or moved by a robotic arm. The proposed approach of using fusion of 3D point clouds from multiple sensors using color segmentation and voxel grid merging for fast surface area coverage computation, coupled with a random sample consensus algorithm for best views estimation, managed to quickly estimate useful sensor constellations for maximizing the observable surface area of a set of target objects, making it suitable to be used for deciding the type and spatial disposition of sensors and also guide movable 3D cameras for avoiding environment occlusions.

Abstract
Robotics in agriculture faces several challenges, such as the unstructured characteristics of the environments, variability of luminosity conditions for perception systems, and vast field extensions. To implement autonomous navigation systems in these conditions, robots should be able to operate during large periods and travel long trajectories. For this reason, it is essential that simultaneous localization and mapping algorithms can perform in large-scale and long-term operating conditions. One of the main challenges for these methods is maintaining low memory resources while mapping extensive environments. This work tackles this issue, proposing a localization and mapping approach called VineSLAM that uses a topological mapping architecture to manage the memory resources required by the algorithm. This topological map is a graph-based structure where each node is agnostic to the type of data stored, enabling the creation of a multilayer mapping procedure. Also, a localization algorithm is implemented, which interacts with the topological map to perform access and search operations. Results show that our approach is aligned with the state-of-the-art regarding localization precision, being able to compute the robot pose in long and challenging trajectories in agriculture. In addition, we prove that the topological approach innovates the state-of-the-art memory management. The proposed algorithm requires less memory than the other benchmarked algorithms, and can maintain a constant memory allocation during the entire operation. This consists of a significant innovation, since our approach opens the possibility for the deployment of complex 3D SLAM algorithms in real-world applications without scale restrictions.

Abstract
Reinforcement Learning (RL) is a well-suited paradigm to train robots since it does not require any previous information or database to train an agent. This paper explores using Deep Reinforcement Learning (DRL) to train a robot to navigate in maps containing different sorts of obstacles and which emulate hallways. Training and testing were performed using the Flatland 2D simulator and a Deep Q-Network (DQN) provided by OpenAI gym. Different sets of maps were used for training and testing. The experiments illustrate how well the robot is able to navigate in maps distinct from the ones used for training by learning new behaviours (namely following walls) and highlight the key challenges when solving this task using DRL, including the appropriate definition of the state space and reward function, as well as of the stopping criteria during training.