A survey on coverage path planning for robotics <s> Obstacle neuron Uncovered area neuron <s> This article concludes a series of papers concerned with the flow of electric current through the surface membrane of a giant nerve fibre (Hodgkinet al., 1952,J. Physiol. 116, 424–448; Hodgkin and Huxley, 1952,J. Physiol. 116, 449–566). Its general object is to discuss the results of the preceding papers (Section 1), to put them into mathematical form (Section 2) and to show that they will account for conduction and excitation in quantitative terms (Sections 3–6). <s> BIB001 </s> A survey on coverage path planning for robotics <s> Obstacle neuron Uncovered area neuron <s> In this paper, a novel complete coverage path planning method based on the biologically inspired neural networks, rolling path planning and heuristic searching approach is presented for mobile robot motion planning with obstacles avoidance. The biologically inspired neural network is used to model the environment and calculate the environment information, while the rolling planning technique and the heuristic searching algorithm are utilized for the path planning,. Simulation studies show that the proposed method is very effective for the dynamic uncertain environments. <s> BIB002 </s> A survey on coverage path planning for robotics <s> Obstacle neuron Uncovered area neuron <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> BIB003
Covered area neuron Robot Figure 22 : Schematic of the neural network used by Luo, Yang and others to achieve coverage. A shunting equation based on the membrane equation by Hodgking and Huxley BIB001 determines the dynamics of each neuron in the network. The activity landscape (i.e., the output value of all neurons at a given instant) of the shunting model used attracts the robot to unclean areas, while the robot is repulsed by already cleaned areas and obstacles. The next position of the robot is determined by the current position of the robot and the activity of the neuron associated to its current position, without any prior knowledge about the environment. It is assumed that the current state of the robot (if it's in a clean or dirty area, or in front of an obstacle, and its location) can be determined via sensory information. The state of the robot is an input to the neural network. The model used has 6 parameters that can be tuned in a wide range of values at the neural network design phase, and hence coverage is achieved without any learning procedures. An advantage of this method is that it can handle non-stationary environments (i.e., dynamically changing obstacles). The proposed neural network approach is validated in simulation. In BIB003 further simulation results are presented as well as a method to perform mapping on-line simultaneously with coverage navigation. In this later work, they consider a typical grid-based map and also a triangular mesh representation of the space, such as the one used in  ]. An application of this neural network-based method to an autonomous underwater vehicle covering a 2-D workspace in the seabed is proposed in  . The proposal is validated in simulation. However, a 2-D underwater workspace is rather unrealistic. Furthermore, discretization in a grid map of a vast environment such as the seabed presents a tough challenge in terms of computational burden. [ BIB002 ] add a local path planning technique on top of the biologically inspired neural network approach discussed above. In their approach, the next robot position is not determined immediately but rather a local path planning occurs in a window comprising a determined vicinity of the robot. By using this technique, they reduce the computational burden in comparison with the neural-network-only approach. In a similar approach,  ] present a neural network-based method to generate continuous steering control for a robot to completely cover a bounded region over a finite time. First, they discretize the space in a regularly spaced, disk-shaped grid. Then, a neural network based on the same biologically inspired shunting equation as in the works discussed above is used to provide continuous steering to the robot. The algorithm works for car-like robots which have nonholonomic motion constraints. The approach is validated in simulation.   presented an on-line coverage method for robots equipped with sidelooking sensors. Their target application is mine countermeasure operations using an autonomous underwater vehicle equipped with a side-scan sonar. This coverage method continuously directs the vehicle's heading using multi-objective optimization to maximize the information gain produced by the sensor measurements. The optimization procedure uses a grid decomposition composed of uniform hexagonal cells. The advantage of using a hexagonal grid is two-fold. On one hand, distance does not need to be taken into account in the objective functions, because the distance from a given cell to its neighboring cells is the same. On the other hand, assuming the hexagons are small enough, visiting one cell guarantees coverage of two neighboring cells by the side-looking sensor, minimizing the amount of partially covered cells. Although the proposed method is able to cover target areas with non-convex shapes, obstacles present amidst the workspace are not considered. The efficacy of this method is demonstrated in simulation and experimentally on an autonomous underwater vehicle conducting mine countermeasure operations.