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Path Planning for Autonomous Underwater Vehicles

Depending on the objective, two types of path planning and simulation of unmanned underwater device trajectories should be allocated. These are the path from one point to the other and the complete grid coverage of the space (Panda et al., 2020). The initial analysis of both scenarios gives a vivid distinction between them: the declared aim of the vehicle. The path from point to point seems more primitive as the AUV must perform a straight-line motion in the water environment to the finish point. Nevertheless, more complex intelligent mechanisms of the machine allow it to bend an obstacle on the way, which actually makes the trajectory curved, but still one-dimensional. One can assume that the tasks of such design are to determine the distance between the points, safety, or smoothness of a particular route (Xue & Sun, 2018). Even if one admits that a two-point path is unrestricted and free of pre-planning intermediate points, grid designing is a more successful but time-consuming strategy. With this option, AUV should perform real-time space analysis to detect obstacles: this logic is the basis of most radars (Zhu et al., 2019). Accordingly, the overall objective of such design is to create a broad picture of underwater space for use in mapping purposes.

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Regardless of the type of design, AUVs have a severe problem associated with increasing the environment’s pressure closer to the bottom of the reservoir. Sensitive electronics of the device can get out of control at high pressures, which means there is a need to isolate and seal the essential components of the device. On the other hand, the two-point path can be broken by obstacles in the way of AUV, especially if the device cannot overcome the barriers on its own. Currents also play a significant role in performance since the oncoming water flows force the device to use high engine power. For AUV, which analyzes the space on the principle of a grid, the same problems are true, but because of the more significant number of objects of analysis, the device can create a blurred picture. In particular, underwater shadows, gravity distortion, or bottom objects can be perceived as part of the space, which will deteriorate the overall appearance.

References

Panda, M., Das, B., Subudhi, B., & Pati, B. B. (2020). A Comprehensive review of path planning algorithms for autonomous underwater vehicles. International Journal of Automation and Computing, 17(3) 1-32. Web.

Xue, Y., & Sun, J. Q. (2018). Solving the path planning problem in mobile robotics with the multi-objective evolutionary algorithm. Applied Sciences, 8(9), 1425-1446. Web.

Zhu, D., Tian, C., Sun, B., & Luo, C. (2019). Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm. Journal of Intelligent & Robotic Systems, 94(1), 237-249. Web.

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StudyCorgi. (2022) 'Path Planning for Autonomous Underwater Vehicles'. 4 March.

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