A survey on coverage path planning for robotics <s> Grid-based Coverage <s> Abs t rac t Much of the focus of the research effort in path planning for mobile robots has centred on the problem of finding a path from a start location to a goal location, while minimising one or more parameters such as length of path, energy consumption or journey time. A path of complete coverage is a planned path in which a robot sweeps all areas of free space in an environment in a systematic and efficient manner. Possible applications for paths of complete coverage include autonomous vacuum cleaners, lawn mowers, security robots, land mine detectors etc. This paper will present a solution to this problem based upon an extension to the distance transform path planning methodology. The solution has been implemented on the self-contained autonomous mobile robot called the Yamabico. <s> BIB001 </s> A survey on coverage path planning for robotics <s> Grid-based Coverage <s> We describe an on-line sensor based algorithm for covering planar areas by a square-shaped tool attached to a mobile robot. Let D be the tool size. The algorithm, called Spiral-STC, incrementally subdivides the planar work-area into disjoint D-size cells, while following a spanning tree of the resulting grid. The algorithm covers general grid environments using a path whose length is at most (n + m)D, where n is the number of D-size cells and m /spl les/ n is the number of boundary cells, defined as cells that share at least one point with the grid boundary. We also report that any on-line coverage algorithm generates a covering path whose length is at least (2 - /spl epsiv/)l/sub opt/ in the worst case, where l/sub opt/ is the length of the optimal covering path. Since (n + m)D /spl les/ 2l/sub opt/, Spiral-STC is worst-case optimal. Moreover, m << n in practical environments, and the algorithm generates close-to-optimal covering paths in such environments. Simulation results demonstrate the spiral-like covering patterns typical to the algorithm. <s> BIB002 </s> A survey on coverage path planning for robotics <s> Grid-based Coverage <s> Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths. <s> BIB003 </s> A survey on coverage path planning for robotics <s> Grid-based Coverage <s> Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autonomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh et al., 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments. <s> BIB004 </s> A survey on coverage path planning for robotics <s> Grid-based Coverage <s> Tasks such as street mapping and security surveillance seek a route that traverses a given space to perform a function. These task functions may involve mapping the space for accurate modeling, sensing the space for unusual activity, or searching the space for objects. When these tasks are performed autonomously by robots, the constraints of the environment must be considered in order to generate more feasible paths. Additionally, performing these tasks in the real world presents the challenge of operating in dynamic, changing environments. ::: This thesis addresses the problem of effective graph coverage with environmental constraints and incomplete prior map information. Prior information about the environment is assumed to be given in the form of a graph. We seek a solution that effectively covers the graph while accounting for space restrictions and online changes. For real-time applications, we seek a complete but efficient solution that has fast replanning capabilities. ::: For this work, we model the set of coverage problems as arc routing problems. Although these routing problems are generally NP-hard, our approach aims for optimal solutions through the use of low-complexity algorithms in a branch-and-bound framework when time permits and approximations when time restrictions apply. Additionally, we account for environmental constraints by embedding those constraints into the graph. In this thesis, we present algorithms that address the multi-dimensional routing problem and its subproblems and evaluate them on both computer-generated and physical road network data. <s> BIB005
Wavefront Algorithm BIB001 Off-line Grid-discretized Simple, easy to implement algorithm. Spiral-STC Algorithm BIB002 On-line Grid-discretized Minimizes repeated coverage by visiting each grid cell only once. Neural Network BIB003 BIB004 On-line Grid-discretized Handles dynamic obstacles. Hexagonal Grid  On-line Grid-discretized Maximizes information gain along the path. Graph Coverage Various graph search algorithms BIB005 On-line Environmental constraints Applies to environments that can be represented as a graph, such as a street or road network.