A Review on Sensor Network Issues and Robotics <s> Coverage for Multirobots <s> Motivated by potential efficiency and robustness gains, there is growing interest in the use of multiple robots for coverage. In coverage, robots visit every point in a target area, at least once. Previous investigations of multi-robot coverage focus on completeness of the coverage, and on eliminating redundancy, but do not formally address robustness. Moreover, a common assumption is that elimination of redundancy leads to improved efficiency (coverage time). We address robustness and efficiency in a novel family of multi-robot coverage algorithms, based on spanning-tree coverage of approximate cell decomposition of the work-area. We analytically show that the algorithms are robust, in that as long as a single robot is able to move, the coverage will be completed. We also show that non-redundant (non-backtracking) versions of the algorithms have a worst-case coverage time virtually identical to that of a single robot-thus no performance gain is guaranteed in non-redundant coverage. Surprisingly, however, redundant coverage algorithms lead to guaranteed performance which halves the coverage time even in the worst case. We present a polynomial-time redundant coverage algorithm, whose coverage time is optimal, and which is able to address robots heterogeneous in speed and fuel. We compare the performance of all algorithms empirically and show that the use of the optimal algorithm leads to significant improvements in coverage time. <s> BIB001 </s> A Review on Sensor Network Issues and Robotics <s> Coverage for Multirobots <s> We investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the floor of an un-mapped building, or any task that requires the traversal of an unknown region. More specifically, we consider robots which leave chemical odour traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement error. Our abstract model is a decentralized multi-agent adaptive system with a shared memory, moving on a graph whose vertices are the floor-tiles. We describe three methods of covering a graph in a distributed fashion, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. Our algorithms can complete the traversal of the graph even if some of the agents die or the graph changes during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time. <s> BIB002 </s> A Review on Sensor Network Issues and Robotics <s> Coverage for Multirobots <s> Multi Robot Systems are, nowadays, an important research area within Robotics and Artificial Intelligence. Although Multi Robot Systems can be regarded as a particular case of Multi Agent Systems, it seems appropriate to study the Multi Robot Systems from a specific viewpoint, because of the issues which arise from the embodiment of agents operating in real environments. In this paper, we present an analysis of Multi Robot Systems by looking at their cooperative aspects. In particular, we propose a taxonomy of Multi Robot Systems and a characterization of reactive and social deliberative behaviors of the Multi Robot System as a whole. Finally, we address some Multi Robot Systems, which we consider representative of the various nodes in our taxonomy. <s> BIB003 </s> A Review on Sensor Network Issues and Robotics <s> Coverage for Multirobots <s> The problem of coverage without a priori global information about the environment is a key element of the general exploration problem. Applications vary from exploration of the Mars surface to the urban search and rescue (USAR) domain, where neither a map, nor a Global Positioning System (GPS) are available. We propose two algorithms for solving the 2D coverage problem using multiple mobile robots. The basic premise of both algorithms is that local dispersion is a natural way to achieve global coverage. Thus, both algorithms are based on local, mutually dispersive interaction between robots when they are within sensing range of each other. Simulations show that the proposed algorithms solve the problem to within 5–7% of the (manually generated) optimal solutions. We show that the nature of the interaction needed between robots is very simple; indeed anonymous interaction slightly outperforms a more complicated local technique based on ephemeral identification. <s> BIB004 </s> A Review on Sensor Network Issues and Robotics <s> Coverage for Multirobots <s> In this paper, we proposed an improved algorithm for the multi-robot complete coverage problem. Real world applications such as lawn mowing, chemical spill clean-up, and humanitarian de-mining can be automated by the employment of a team of autonomous mobile robots. Our approach builds on a single robot coverage algorithm, Boustrophedon decomposition. The robots are initially distributed through space and each robot is allocated a virtually bounded area to cover. The area is decomposed into cells where each cell width is fixed. The decomposed area is represented using an adjacency graph, which is incrementally constructed and shared among all the robots. Communication between the robots is available without any restrictions. Experiments on both simulated and physical hardware demonstrated the viability of employing the algorithm to perform distributed coverage of a given unknown area with multiple robots <s> BIB005 </s> A Review on Sensor Network Issues and Robotics <s> Coverage for Multirobots <s> We demonstrate the realization of stigmergic coverage for multi-robot systems. Compared to current state-of-the-art algorithms for multi-robot coverage, our Stigmergy-based Coverage algorithm (StiCo) has several key advantages. In particular, it does not need direct robot-robot communication. Moreover, this algorithm does not require any prior information about the environment. Simulation results illustrate robustness, scalability and simplicity of the algorithm. <s> BIB006
The use of multirobots holds numerous advantages over a single robot system. Their potential of doing work is way far better than that of single robot system BIB003 . Multiple robots can increase the robustness and the flexibility of the system by taking benefits of redundancy and inherent parallelism. They can also cover an area more quickly than a single robot and they have also potential to accomplish a single task faster than a single robot. Multiple robots can also localize themselves more efficiently when they have different sensor capabilities. Coverage for multirobot systems is an important field and is vital for many tasks like search and rescue, intrusion detection, sensor deployment, harvesting and mine clearing, and so forth  . To get the coverage the robots must be capable of spotting the obstacles in the environment and they should also exchange their knowledge of environment and have a mechanism to assign the coverage tasks among themselves BIB005 . The problem of deploying a mobile senor network into an environment was addressed in BIB004 with the task of maximizing sensor coverage and also two behavior based techniques for solving the 2D coverage problems using multiple robots were proposed. Informative and molecular techniques are the techniques proposed for solving coverage problems and both of these techniques have the same architecture. When robots are within the sensor range of each other, the informative approach is to assign local identities to them. This approach allows robots to spread out in a coordinated manner because it is based on ephemeral identification where temporary local identities are assigned and mutual local information is exchanged. No local identification is made in molecular approach and also robots do not perform any directed communication. Each robot moves in a direction without communicating its neighbors because it selects its direction away from all its immediate sensed neighbors. Then these algorithms were compared with another approach known as basic approach, which only seeks to maximize each individual robot's sensor coverage  . Both these approaches perform significantly better than basic approach and with the addition of few robots the coverage area quickly maximizes. An algorithm named (StiCo) which is an coverage algorithm was proposed for multirobot systems in BIB006 . This algorithm is based on the principle of stigmergic (pheromone-type) coordination known from the ant societies where a group of robots coordinate indirectly via ant-like stigmergic communication. This algorithm does not require any prior information about the environment and also no direct robot-robot communication is required. Similar kind of approach was used by Wagner et al. BIB002 for coverage in multirobot in which a robot deposits a pheromone which could then be detected by other robots; these pheromones come up with a decay rate, allowing continuous coverage of an area via implicit coordination BIB005 . For multirobot coverage, BIB005 proposed boustrophedon decomposition algorithm in which the robots are initially distributed through space and each robot is allocated at virtually bounded area to cover the area and is then decomposed into cells with the fixed cell width. By using the adjacency graph the decomposed area is represented which is incrementally constructed and shared among all robots and without any restriction robot communication is also available. By sharing information regularly and task selection protocol performance is improved. By planting laser beacons in environment the problem of localization in the hardware experiment was overthrown and using the laser range finder to localize the robots as this was the major problem to guarantee accurate and consistent coverage. Based on spanning-tree coverage of approximate cell decomposition, robustness and efficiency in a family of multirobot coverage algorithms was addressed BIB001 . Their approach is based on offline coverage: it is assumed that the robots have a map of the area a priori. The algorithm they proposed decomposes the work area into cells, where each cell is a square of size 4D and each cell is then further broken down into quadrants of size D.