The map being built is used in order to improve the search of the dynamic agents.The rest of the paper is organized as follows. First, in the next section we present some work in close relation with this paper. Second, the grid-based Bayesian filter used to estimate the best target areas is explained in Section 3. Next, the coordinated goal selection for each agent besides of the Search Team is developed Inhibitors,Modulators,Libraries in Section 4. Then the experiments carried out to test the approach are exposed in Section 5. Finally, Section 6 explains the conclusions and open research lines.2.?Related WorkIn mobile robotics, exploration is considered as the problem of traversing all the areas of a particular unknown environment [3].
Normally, exploration algorithms are used jointly with a Simultaneous Localization and Mapping (SLAM) algorithm in order to create a map of the environment during the exploration process [5]. Classical exploration techniques direct the robots to examine every place in the environment in an efficient manner, Inhibitors,Modulators,Libraries thus reducing the total time needed to cover a determined area. For instance, some algorithms command the robots to the nearest frontier [6]. In these techniques, the environment is represented in a discrete bi-dimensional grid, where each cell is considered as occupied, free or unknown (unexplored). Frontier cells are unknown cells that lie next to a free cell. The application of this family of algorithms to search problems is direct [7].When the main objective is to incrementally build a map of the environment, exploration strategies allow to select the vantage points that should be reached by the robots.
In these activities, it is of capital importance to design a good exploration strategy in order to build the most precise map in the shortest Inhibitors,Modulators,Libraries time. Most of the exploration algorithms presented to date consider the evaluation of an utility function in order to select the best trajectory for the robot [8]. Some authors have extended these ideas to the case of multi-robot exploration. Commonly, the utility function used in exploration is a measure of the information gain of visiting a place in the environment and the cost of reaching it. The cost is in relation with the distance between the robot and the possible destination, whereas the utility is estimated in dissimilar manners. For example, Inhibitors,Modulators,Libraries in [9] utility is considered as the visible area behind a frontier.
In [10], the destinations chosen by other robots are taken into account in the computation of the utility function. Thus, when a robot is commanded to a point in the environment, the utility of this area is reduced for the rest of Anacetrapib the robots, necessary in an effort to coordinate them and increase the exploration speed. A market-based mechanism where the robots compete and optimize their routes by negotiating their destinations as a function of the cost and the expected utility was suggested in [11].
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