This paper presents two efficient methods for obstacle avoidance and path planning for Autonomous Underwater Vehicle (AUV). These methods take into account the dynamic constraints of the vehicle using advanced simulator of AUV considering low level control and stability effects. We present modified visibility graph local avoidance method and a spiral algorithm for obstacle avoidance. The algorithms were tested in challenged scenarios demonstrating safe trajectory planning. 1. Introduction Path planning and obstacle avoidance are an important issues for Autonomous Underwater Vehicles (AUVs), and as can be noticed lately, these fields are extensively studied. Currents disturbances can have a big influence in a different water depth and should be considered in underwater environments [1]. For the first time, Soulignac et al. [2] faced with strong current situations. Online replanning in 3D underwater environments with strong, dynamic, and uncertain currents was presented in [3]. AUV also suffers from a limited energy source, which can be minimized by planning optimal trajectories, extending the working time of the vehicle. Optimal path planning using A* algorithm search was presented by Carroll et al. [4]. Garau et al. [5]. Pêtrès et al. [6, 7] used similar concepts using BF (breadth first) search named FM and FM*. As the presented methods in this paper, other methods do not use grid-based search. The path is presented with a series of points, which are connected one by one. Path planning problem transformed to a constrained optimization problem in terms of the coordinates of these points, generating optimal paths considering AUV’s dynamic constraints [1, 8–10]. One of the most known limitations in motion planning algorithms related to real-time computation ability. Planning methods based on local perceptions are computationally less expensive and thus time efficient. Bui and Kim [11] and Kanakakis et al. [12] apply fuzzy logic approaches to AUV path planning. Antonelli et al. [13, 14] integrate virtual force field (VFF); all of these are not optimal planning paths methods. This paper presents several different AUV path planning algorithms avoiding obstacle based on local perception abilities based on forward looking sonar. The introduced algorithms inherently take into account AUVs dynamic and kinematic constraints. AUV trajectory is simulated as described later. Simulations in typical underwater environments are presented, demonstrating algorithm’s capabilities. 2. Vehicle Simulator AUV platforms are known as underactuated vehicles models; these kinds of
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