Highlight Review| Path Planning Algorithms for Autonomous Underwater Vehicles

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Brief Introduction

Autonomous underwater vehicles (AUVs) are considered as a substantial group of submerged systems known as “unmanned underwater vehicles (UUVs)”. UUVs are generally classified as AUV and remotely operated vehicle (ROV). ROVs are powered and operated from a surface control station by an umbilical cord or remote control. AUVs carry their independent onboard power supply. They are cylindrical in shape and do not have attached cables. AUV designs include “torpedo-like geometry”, “gliders” and “hovering”. AUVs scale from portable to huge sizes of hundred tons. An AUV is a highly nonlinear robotic vessel, whose dynamic equation include square terms due to hydrodynamic damping factors. It can operate both above and beneath the ocean′s surface. The AUV propagates by changing its buoyancy in small steps, thereby converting the resultant vertical displacement to horizontal movement. This is accomplished by the interactivity between the surface control station and the water column.

 

Motion planning is familiarly associated with “path planning (PP)” and “trajectory planning (TP)”. Path planning is finding the course of points across which AUV has to travel to reach the predefined destination from the starting location, whereas the time history of this journey of the AUV is referred to as trajectory planning. AUV navigation is a very important aspect of path planning. No external communication and “global positioning system (GPS)” signals are available in underwater environments. Thus, without information of direction and restricted power, it is very difficult for an AUV to navigate towards the desired target. Three primary navigation methods have been suggested for AUVs that are “deadreckoning and inertial navigation systems (DR-INS), acoustic navigation and geophysical navigation”. Referring to the literature available on AUV navigation, one can distinguish three different problems that are “closeto-surface navigation, navigation in the mid-depth zone, close-to-bottom navigation”.

 

In the path planning control (PPC) problem, an AUV has to traverse a convergent path without temporal constraints. Earlier works on PPC of wheeled robots solved two major issues reported as “path parameterization” and the selection of the termination point on the path. A control system for the coordinated operation of an “autonomous surface craft (ASC)” and an AUV has been designed by Encarnacao and Pascoal, which is based on combined trajectory tracking and path planning control.

 

The underwater environment plays a significant part in the path planning of AUVs. The sea environment is subjected to a large set of challenging factors such as atmospheric factors, coastal factors and gravitational factors. Atmospheric factors include winds, sunlight and precipitation. Coastal factors deal with rivers, glaciers, and gravitational factors include earth rotation, seabed and tides. Navigation of an AUV is majorly affected by wind generated waves, wind and oceanic current. The effect of oceanic current needs much consideration in path determination. The ocean environment is unpredictable and time-varying, but sometimes the effects of the environmental factors can be approximated to produce a predictable behavioral model of the underwater environment. The environment is considered as unpredictable when the changes in the environment are uncertain or unknown. Hence, the underwater environment can be characterized as predictable and unpredictable. A qualitative analysis of different path planning algorithms used in AUV PP is presented in this paper. Various algorithms are reviewed for both single and multiple AUVs based on predictable and unpredictable ocean environments. This review is expected to be very useful for future researchers from the qualitative analysis of different path planning control (PPC) techniques employed in the area of AUV path planning and their merits, demerits and scope of avoiding the difficulties.

 

This review is organized as follows. Section 2 reviews the different methodologies employed for the path planning task of a single AUV. Section 3 describes the path planning strategies for multiple AUVs and the paper is concluded in Section 4.

 

Conclusion

This survey presents a qualitative analysis of the impact of the marine environment on the path planning of AUVs. The underwater environment is characterized as predictable and unpredictable depending on path planning approximations. This paper summarizes the available path planning algorithms employed for single and multiple AUVs with reference to predictable and unpredictable behavioral models of the environment. The issues involved in path planning of AUVs are discussed briefly. The algorithms are compared considering the type of the environment, type of the path generated, path cost and collision avoidance features. Merits and demerits of every method have been discussed briefly. Type of path generated by the methods are classified as time optimal (time minimal solution), energy optimal (energy minimal solution), sub-optimal (near optimal solution) and optimal (best possible solution). Path costs are compared as low, moderate and high. Collision and obstacle avoidance are discussed as achieved, limited and poor based on whether the algorithm focused on these issues or not. Based on this study, we can conclude that the issues of unreliability have not been addressed much in the studied literature. Many assumptions are taken for AUV dynamics and operating environment, which are required to be critically analyzed for stability in real world scenarios. Thus, there is a need for formulating optimized algorithms in the future that is computationally efficient and rugged for real time applications of AUVs.

 

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A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Madhusmita Panda, Bikramaditya Das, Bidyadhar Subudhi, Bibhuti Bhusan Pati

Full text:

http://www.ijac.net/en/article/doi/10.1007/s11633-019-1204-9 

https://link.springer.com/article/10.1007/s11633-019-1204-9 

 

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