Anderson A. S. Souza

Increasing Students' Interest With Low-Cost CellBots

This paper introduces the use of a flexible and affordable educational robot specifically developed for the practical experimentation inherent to technological disciplines. The robot has been designed to be reconfigurable and extendible, serving as an experimental platform across several undergraduate courses. As most students have a mobile cell phone, this was used as the main control computer for the so-called CellBot, thus avoiding any need to deal with the details of microcontrollers or other embedded computing devices. Assessment results are also presented, based on a pre- and post-survey of student opinion administered to 204 science and engineering students from several universities. Among the conclusions are that 83% of the students prefer to use these low-cost robots as tools to improve their learning of the theory in several disciplines, and 71% of the students stated that they prefer to have their own robot to experiment with, instead of using a didactic kit loaned to them by the university.

2.5-Dimensional Grid Mapping from Stereo Vision for Robotic Navigation

In this paper we propose a new method for environment mapping with three-dimensional information from visual information for robotic accurate navigation. Many approaches of 3D mapping using occupancy grid typically requires high computational effort to both build and store the map. We introduce an occupancy-elevation grid mapping, which is a discrete mapping approach, where each cell stores the occupancy probability, the height of the terrain at current place in the environment and the variance of this height value. This 2.5-dimensional representation allows that a mobile robot to know whether a place in the environment is occupied by an obstacle and the height of this obstacle, thus, it can decide if is possible to traverse the obstacle. Sensorial information necessary to construct the map is provided by a stereo vision system, which has been modelled with a robust probabilistic approach, considering the noise present in the stereo processing. The resulting maps favours the execution of tasks like decision making in the autonomous navigation, exploration, localization and path planning. Experiments carried out with a real mobile robots demonstrates that this proposed approach yields useful maps for robot autonomous navigation.

Probabilistic robotic grid mapping based on occupancy and elevation information

This paper proposes an alternative environment mapping method for accurate robotic navigation based on 3D information. Typical techniques for 3D mapping using occupancy grid require intensive computational workloads to both build and store the map. We introduce an occupancy-elevation grid (OEG) mapping technique, which is a discrete mapping approach where each cell represents the occupancy probability, the height of the terrain and its variance. This representation allows a mobile robot to know if a spot in the environment is occupied by an obstacle and the height of such obstacle, thus, it can decide if it is possible to traverse the obstacle. In general, the map representation introduced can be used in conjunction with any kind of distance sensor. In this work, we have used laser range data with a probabilistic treatment. The resulting maps allow the execution of tasks like decision making for autonomous navigation, exploration, localization and path planning, considering the existence and the height of the obstacles. Experiments carried out with real data demonstrate that the proposed approach yields useful maps for autonomous navigation.

3D Probabilistic Occupancy Grid to Robotic Mapping with Stereo Vision

Environment mapping is considered an essential skill for a mobile robot in order to actually reach autonomy [1]. The robotic mapping can be defined as the process of acquiring a spa‐ tial model of the environment through sensory information. The environment map allows mobile robots to interact coherently with objects and people in this environment. The robot can safely navigate, identify surrounding objects and have flexibility to dealing with unex‐ pected situations. Without a map some important operations could be complex as the deter‐ mination of objects position in the surroundings of the robot and the definition of the path to be followed. These issues involve the importance of the mapping task be performed cor‐ rectly, since the acquisition of inaccurate maps can lead to errors in the inference of correct positioning of the robot, resulting in an imperfect implementation of these operations. Therefore there is a mutual dependence between inferring the exact localization of the robot and building an accurate map.

Occupancy-elevation grid: an alternative approach for robotic mapping and navigation

distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. doi:10.1017/S0263574715000235 Occupancy-elevation grid: an alternative approach for robotic mapping and navigation Anderson Souza†∗ and Luiz M. G. Gonçalves‡

Adaptive Navigation for Mobile Robots with Object Recognition and Ontologies

The ability of a mobile robot to navigate in unknown and dynamic environments depends on some issues, like localization, sensors data processing (or mapping) and decision making. According to the environment characteristics, the robot needs to make different decisions, aiming to finish its mission, to preserve its physical integrity and the environment integrity. This paper presents an intelligent navigation system based on objects recognition and its semantics. The proposed system permits a mobile robot to assume different behaviors according to the recognized objects and their properties. Furthermore, the mobile robot can predict some behavior inherent to the recognized objects, helping it to interact coherently with its environment and objects. In order to achieve this purpose, the presented system uses the SURF algorithm to perform object recognition over Kinect images and ontologies techniques to define an object and its properties. At the end of this paper some preliminary experiments are shown with real data.

Occupancy-Elevation Grid Mapping from Stereo Vision

This paper proposes an alternative environment mapping method for accurate robotic navigation based on 3D information. Typical techniques for 3D mapping using occupancy grid require intensive computational workloads to both build and store the map. We introduce an Occupancy-Elevation Grid (OEG) mapping based on visual range data, which is a discrete mapping approach where each cell represents the occupancy probability, the elevation of the terrain and its variance. This representation allows a mobile robot to know if a space in its environment is occupied by an obstacle and the elevation of such obstacle, thus, it can decide if it is possible to traverse the obstacle. The resulting maps allow the execution of tasks like decision making for autonomous navigation, exploration, localization and path planning. Experiments carried out with a real mobile robot equipped by a stereo vision system demonstrate that the proposed approach yields useful maps for autonomous robust navigation.

Probabilistic Mapping by Fusion of Range-Finders Sensors and Odometry

One of the main challenges faced by robotics scientists is to provide autonomy to robots. That is, according to Medeiros (Medeiros, 1998) a robot to be considered autonomous must present a series of abilities as reaction to environment changes, intelligent behavior, integration of data provided by sensors (sensor fusion), ability for solving multiple tasks, robustness, operation without failings, programmability, modularity, flexibility, expandability, adaptability and global reasoning. Yet in the context of autonomy, the navigation problem appears. As described in Fig. 1, sense, plan and act capabilities have to be previously given to a robot in order to start thinking on autonomous navigation. These capabilities can be divided into sub-problems abstracted hierarchically in five levels of autonomy: Environment Mapping, Localization, Path Planning, Trajectory Generation, and Trajectory Execution (Alsina et. al., 2002).

GTMV: Virtual museum authoring systems

In this work we propose a system that has joined the multi-user system paradigm and the easy-to-use authoring tools for the development of virtual museums through the Web. Our system provides an interface for authoring and another interface for navigation. The authoring interface is used by the curator and has tools for building and editing museums. The navigator interface is used by visitors to interact with the museum including its objects and other users.

A Multimedia Framework for Collaborative Interaction of People and Robots Through the Web

We propose a multimedia framework built on a flexible architecture designed for collaborative interaction of people and robots through the Web. Users can communicate to each other exchanging text, audio and video messages. Besides, it provides a flexible interface for people to interact with robots, for robots with people, and among robots themselves, for example, allowing a robot to communicate to another one. One or several users can communicate with one or more robots and also receive information from them. Robots can be remotely controlled through the use of keyboard, joysticks, data glove, and voice commands. A user can perceive other users through a virtual environment and receive the video from an environment, in which the robot is