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Due to rapid changes in modern technological development over the last several years, researches on small Autonomous Underwater Vehicles (AUVs) and Unmanned Underwater Vehicles (UUVs) appeared as important issues for various possibilities of application in the ocean. Military applications together with commercial need require practical details which are robust and cheap for realization as a product. In addition, works on coverage have been motivated by a wide range of real world applications that include non-humanitarian demining, deep-sea development sweeping and robotic spray-painting. Currently, many coverage applications utilize on-line coverage algorithms (Gabriely et al., 2003), where robots do not rely on a priori knowledge of a work-area, and thus must construct their motion trajectories step-by-step, addressing discovered obstacles as they move. This is the main contrast from the conventional off-line coverage algorithms, where robots are given a map of a work-area, and can therefore plan their paths ahead of deployment (Ge et al., 2005). In this paper, we focus on on-line coverage for underwater environment by multiple AUVs. Relevant works have shown that one of advantages of adopting multiple robots for a coverage task is the potential ability for more efficient coverage (Hazon et al, 2005). However, another advantage is that they usually offer greater robustness. Unfortunately,this important capability has been neglected in previous works done on on-line algorithms. As far as the details of the on-line coverage methods, there are many kinds of covering motions that can be realized either by back-and-forth motions in vast cells, or by following a general Voronoi diagram in narrow cells (Acar et al., 2001). Exact cellular decomposition can also be achieved through the use of the boustrophedon decomposition (Choset, 2001) as well as through Morse functions (Acar, 2002). The boustrophedon approach has been extended in (Choset, 2001) to the multi-robot domain. Such form of coverage requires the coverage to be executed in formations, which may be accomplished in a variety of ways (Ge et al., 2005). Spanning Tree Coverage algorithms (Gabriely, 2003) have also been proposed for online coverage of an unknown and gridded environment by a robot. For coverage with multi-robot teams, a frontier-based exploration technique has been used to guide robots towards the region between known and unknown areas on the map (Yamauchi et al., 1998). The Mark and Cover (MAC) algorithm has been proposed in (Wagner et al., 2000) with the proved convergence and the bounds on cover time of the algorithm. In (Yang & Luo, 2004), a neural network model has been used for exploration of a O pe n A cc es s D at ab as e w w w .in te hw eb .c om |
