Despite the many research efforts addressing the integration of mobile nodes into grids, only a few of them have considered the establishment of mobile grids over wireless ad hoc networks (hereafter, mobile ad hoc grids). Clearly, such grids need specialized resource discovery and scheduling mechanisms. To the best of our knowledge, though, the research on these mechanisms for mobile ad hoc grids is still preliminary. Besides, and more importantly, it has approached discovery and scheduling as separate mechanisms, which, we argue, is not suitable for mobile ad hoc grids. In this paper, we propose the integration of resource discovery and scheduling for mobile ad hoc grids into a single protocol called DICHOTOMY (DIscovery and sCHeduling prOTOcol for MobilitY). This protocol allows computational tasks to be distributed appropriately in a mobile ad hoc grid, while mitigating the overhead of discovery messages exchanged among the nodes. Our experiments show that the protocol: (i) does proper scheduling, allowing an efficient load balancing among the nodes and helping with lowering the average completion time of tasks; (ii) keeps the discovery efficiency at acceptable levels in mobility scenarios and (iii) scales very well with respect to an increasing number of nodes, both in the total amount of energy savings due to packet transmissions and the distribution of such savings among the nodes.

This article introduces an open-source module responsible for the presentation of verbal (speech) and corporal (animation) behaviors of animated pedagogical agents. This module can be inserted into any learning environment regardless of application domain and platform, being executable under different operating systems. It was implemented in Java as a reactive agent (named Body agent) that communicates with the agent's Mind through a language known as FIPA-ACL. Therefore, it may be inserted into any intelligent learning environment that is also capable to communicate using FIPA-ACL. Persistence of information is ensured by XML files, increasing the agent's portability. The agent also includes a mechanism for automatically updating new behaviors and characters once available in the server. A simulation environment was conceived to test the proposed agent.

The Lattes platform is the major scientific information system maintained by the National Council for Scientific and Technological Development (CNPq). This platform allows to manage the curricular information of researchers and institutions working in Brazil based on the so called Lattes Curriculum. However, the public information is individually available for each researcher, not providing the automatic creation of reports of several scientific productions for research groups. It is thus difficult to extract and to summarize useful knowledge for medium to large size groups of researchers. This paper describes the design, implementation and experiences with scriptLattes: an open-source system to create academic reports of groups based on curricula of the Lattes Database. The scriptLattes system is composed by the following modules: (a) data selection, (b) data preprocessing, (c) redundancy treatment, (d) collaboration graph generation among group members, (e) research map generation based on geographical information, and (f) automatic report creation of bibliographical, technical and artistic production, and academic supervisions. The system has been extensively tested for a large variety of research groups of Brazilian institutions, and the generated reports have shown an alternative to easily extract knowledge from data in the context of Lattes platform. The source code, usage instructions and examples are available at http://scriptlattes.sourceforge.net/.

This paper analyses aspects associated with the development of joint human-agent planning agents, showing that they can be implemented, in a unified way, via a constraint-based ontology and related functions. The constraints' properties have already been used by several planning approaches as an option to improve their efficiency and expressiveness. This work demonstrates that such properties can also be employed to implement collaborative concepts, which are maintained transparent to the planning mechanisms. Furthermore, the use of constraints provides several facilities to the implementation of advanced mechanisms associated with the human interaction, as also demonstrated here.

The computational models of visual attention, originally proposed as cognitive models of human attention, nowadays are being used as front-ends to some robotic vision systems, like automatic object recognition and landmark detection. However, these kinds of applications have different requirements from those originally proposed. More specifically, a robotic vision system must be relatively insensitive to 2D similarity transforms of the image, as in-plane translations, rotations, reflections and scales, and it should also select fixation points in scale as well as position. In this paper a new visual attention model, called NLOOK, is proposed. This model is validated through several experiments, which show that it is less sensitive to 2D similarity transforms than other two well known and publicly available visual attention models: NVT and SAFE. Besides, NLOOK can select more accurate fixations than other attention models, and it can select the scales of fixations, too. Thus, the proposed model is a good tool to be used in robot vision systems.

The cooperative multirobot localization problem consists in localizing each robot in a group within the same environment, when robots share information in order to improve localization accuracy. It can be achieved when a robot detects and identifies another one, and measures their relative distance. At this moment, both robots can use detection information to update their own poses beliefs. However some other useful information besides single detection between a pair of robots can be used to update robots poses beliefs as: propagation of a single detection for non participants robots, absence of detections and detection involving more than a pair of robots. A general detection model is proposed in order to aggregate all detection information, addressing the problem of updating poses beliefs in all situations depicted. Experimental results in simulated environment with groups of robots show that the proposed model improves localization accuracy when compared to conventional single detection multirobot localization.

In this paper, we combine a path planner based on Boundary Value Problems (BVP) and Monte Carlo Localization (MCL) to solve the wake-up robot problem in a sparse environment. This problem is difficult since large regions of sparse environments do not provide relevant information for the robot to recover its pose. We propose a novel method that distributes particle poses only in relevant parts of the environment and leads the robot along these regions using the numeric solution of a BVP. Several experiments show that the improved method leads to a better initial particle distribution and a better convergence of the localization process.

The use of Autonomous Underwater Vehicles (AUVs) for underwater tasks is a promising robotic field. These robots can carry visual inspection cameras. Besides serving the activities of inspection and mapping, the captured images can also be used to aid navigation and localization of the robots. Visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images. It has been used in a wide variety of non-standard locomotion robotic methods. In this context, this paper proposes an approach to visual odometry and mapping of underwater vehicles. Supposing the use of inspection cameras, this proposal is composed of two stages: i) the use of computer vision for visual odometry, extracting landmarks in underwater image sequences and ii) the development of topological maps for localization and navigation. The integration of such systems will allow visual odometry, localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several underwater conditions, as illumination and noise, leading to a promissory and original visual odometry and mapping technique.

As a mobile robot navigates through an indoor environment, the condition of the floor is of low (or no) relevance to its decisions. In an outdoor environment, however, terrain characteristics play a major role on the robot's motion. Without an adequate assessment of terrain conditions and irregularities, the robot will be prone to major failures, since the environment conditions may greatly vary. As such, it may assume any orientation about the three axes of its reference frame, which leads to a full six degrees of freedom configuration. The added three degrees of freedom have a major bearing on position and velocity estimation due to higher time complexity of classical techniques such as Kalman filters and particle filters. This article presents an algorithm for localization of mobile robots based on the complementary filtering technique to estimate the localization and orientation, through the fusion of data from IMU, GPS and compass. The main advantages are the low complexity of implementation and the high quality of the results for the case of navigation in outdoor environments (uneven terrain). The results obtained through this system are compared positively with those obtained using more complex and time consuming classic techniques.

Reinforcement Learning is carried out on-line, through trial-and-error interactions of the agent with the environment, which can be very time consuming when considering robots. In this paper we contribute a new learning algorithm, CFQ-Learning, which uses macro-states, a low-resolution discretisation of the state space, and a partial-policy to get around obstacles, both of them based on the complexity of the environment structure. The use of macro-states avoids convergence of algorithms, but can accelerate the learning process. In the other hand, partial-policies can guarantee that an agent fulfils its task, even through macro-state. Experiments show that the CFQ-Learning performs a good balance between policy quality and learning rate.

Geographic Data Warehouses (GDW) are one of the main technologies used in decision-making processes and spatial analysis, and the literature proposes several conceptual and logical data models for GDW. However, little effort has been focused on studying how spatial data redundancy affects SOLAP (Spatial On-Line Analytical Processing) query performance over GDW. In this paper, we investigate this issue. Firstly, we compare redundant and non-redundant GDW schemas and conclude that redundancy is related to high performance losses. We also analyze the issue of indexing, aiming at improving SOLAP query performance on a redundant GDW. Comparisons of the SB-index approach, the star-join aided by R-tree and the star-join aided by GiST indicate that the SB-index significantly improves the elapsed time in query processing from 25% up to 99% with regard to SOLAP queries defined over the spatial predicates of intersection, enclosure and containment and applied to roll-up and drill-down operations. We also investigate the impact of the increase in data volume on the performance. The increase did not impair the performance of the SB-index, which highly improved the elapsed time in query processing. Performance tests also show that the SB-index is far more compact than the star-join, requiring only a small fraction of at most 0.20% of the volume. Moreover, we propose a specific enhancement of the SB-index to deal with spatial data redundancy. This enhancement improved performance from 80 to 91% for redundant GDW schemas.

With the increasing popularity of web-based map browsers, remotely obtaining a high quality depiction of cartographic information has become commonplace. Most web mapping systems, however, rely on high-capacity servers transmitting pre-rendered tiled maps in raster format. That approach is capable of producing good quality renderings on the client side while using limited bandwidth and exploiting the browser's image cache. These goals are harder to achieve for maps in vector format. In this work, we present an alternative client-server architecture capable of progressively transmitting vector maps in levels-of-detail (LOD) by using techniques such as polygonal line simplification, spatial data structures and, most importantly, a customized memory management algorithm. A multiplatform implementation of this system is described, where the client application is written entirely in JavaScript and processed within the web browser, avoiding the need of external applications or plug-ins. Results of experiments aimed at gauging both the performance and the display quality obtained with the system are presented and explained. Extensions to the system are also discussed, including issues such as level-of-detail versus visual importance tradeoffs and the handling of closed polygonal lines.