3D Global Dynamic Window Approach for Navigation of Autonomous Underwater Vehicles

Abstract

An autonomous unmanned underwater vehicle is a type of marine self-propelled robot that executes some specific mission and returns to base on completion of the task. In order to successfully execute the requested operations, the vehicle must be guided by an effective navigation algorithm that enables it to avoid obstacles and follow the best path. Architectures and principles for intelligent dynamic systems are being developed, not only in the underwater arena but also in related areas where the work does not fully justify the name. The problem of increasing the capacity of systems management is highly relevant based on the development of new methods for dynamic analysis, pattern recognition, artificial intelligence, and adaptation. Among the large variety of navigation methods that presently exist, the dynamic window approach is worth noting. It was originally presented by Fox et al. and has been implemented in indoor office robots. In this paper, the dynamic window approach is applied to the marine world by developing and extending it to manipulate vehicles in 3D marine environments. This algorithm is provided to enable efficient avoidance of obstacles and attainment of targets. Experiments conducted using the algorithm in MATLAB indicate that it is an effective obstacle avoidance approach for marine vehicles.

A modified dynamic window algorithm for horizontal collision avoidance for AUVs

“…Several modifications to the original DW algorithm have been proposed. Some of these include global information in order to handle local minima in the environment, see [10], [11], [12], [13]. In particular, [13] combines the focussed D* (FD*) [14] global planner with the DW algorithm in a hybrid architecture.…”

Dynamic Obstacle Avoidance for Unmanned Underwater Vehicles based on Velocity Obstacle

“…Although these methods can achieve good planning results, they ignore the unknown obstacles in the environment to the robot. The other category is the local path planning methods that rely on the perception of the surrounding environment, such as the artificial potential field method [10], velocity obstacle method [11], and dynamic window method [12], etc. The main drawback of these methods is the lack of global information, resulting in the planned path is often not the global optimum (such as the shortest path, minimum energy consumption), and in serious cases, the target is unreachable.…”

Dynamic Obstacle Avoidance for Unmanned Underwater Vehicles Based on an Improved Velocity Obstacle Method

“…The common collision avoidance methods include artificial aperture method (APF) [ 7 , 8 , 9 ], dynamic window method (DWA) [ 10 ] and behavior method [ 11 ]. They have strong adaptability to local environments and rely on limited sensor information to avoid collisions and have high efficiency.…”

“…In the paper [ 9 ], the proposal makes use of the Artificial Potential Field (APF) method with a Bacterial Evolutionary Algorithm (BEA) to obtain an enhanced flexible path planner method in environments with static and dynamic obstacles, taking all the advantages of using the APF method. Inara [ 10 ] applies DWA to autonomous navigation of AUV in a three-dimensional environment. The paper [ 11 ] proposes a technique for avoiding obstacles based on the behavioral structure.…”