Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents

Abstract

This paper presents an on-line dynamic path re-planning system for an autonomous underwater vehicle (AUV) to enable it to operate efficiently in a spatiotemporal, cluttered, and uncertain environment. The proposed strategy combines path re-planning with an evolutionary algorithm to adapt and regenerate the trajectory during the course of the mission using continuously updated current profiles from on-board sensors, such as a Horizontal Acoustic Doppler Velocity Logger. A quantumbehaved particle swarm optimization (QPSO) algorithm is used with a cost function which is based on the total time required to travel along the path segments accounting for the effect of space-time variable currents. The proposed path planner is designed to generate an optimal trajectory for an AUV navigating through a spatiotemporal ocean environment in the presence of irregularly shaped terrains as well as obstacles whose position coordinates are uncertain. Simulation results show that using the same on-board computation resources, the proposed path re-planning methodology with reuse of information gained from the previous planning history is able to obtain a more optimized trajectory than one relying on reactive path planning. Subsets of representative Monte Carlo simulations were run to analyse the performance of these dynamic planning systems. The results demonstrate the inherent robustness and superiority of the proposed planner based on path re-planning scheme when compared with the reactive path planning scheme.

Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms

“…In other words, the solution of previously optimal path is used as an initial solution for computing the new path and current states of the vehicle are replaced as the new initial boundary conditions. By following this approach, first of all, the vehicle is able to cope with the uncertainties of the operating field and in fact this strategy provides a near closed-loop guidance configuration; that can be supported with a minimal computational burden, as there is no need to compute the path from the scratch, as opposed to reactive planning strategy [27]. The online path planning mechanism is summarized in Fig.3.…”

“…The follower AUV water-reference velocity is set on υ=2.5 (m/s) and by which it starts the rendezvous mission from the red circle indicated on Fig.15. For the current flow, the vortexes radius (ℓ) and strength parameters (ℑ) have been set on as 2.8 m and 12 m/s, respectively [27]; the uncertain static and moving obstacles follows the same concept used in the previous scenario. Fig.15 (Time Step: 1) shows the first path generated by the evolutionary methods.…”

“…A DE based path planner is applied on an underwater glider traveling in a dynamic ocean structure [23,24] and an off-line path planning method based on Quantum-based PSO (QPSO) is offered for path planning of an unmanned aerial vehicle [25]. The online version of the QPSO-based path planner for dealing with dynamic environment is proposed in [26,27].…”

A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents

“…Path planning for USVs can be classified into two categories, namely, reactive approaches (Khatib, 1986, Borenstein and Koren, 1991, Mohanty and Parhi, 2013, Fiorini and Shiller, 1998 where vehicles makes decision en route and deliberative approaches where vehicles follows a predetermined path (Hart et al, 1968, Holland, 1992, Kennedy, 2011. Several computational approaches comprising of evolutionary methods such as Genetic Algorithm (GAs) or Particle Swarm Optimisation (PSO) (Zeng et al, 2015, Aghababa, 2012, graph search techniques (Garau et al, 2005, Singh et al, 2017a, artificial potential field (APF) (Warren, 1990, Singh et al, 2017b and fast marching (FM) (Liu et al, 2017, Petres et al, 2007 have been applied in path planning of marine vehicles.…”

A Path-Planning Strategy for Unmanned Surface Vehicles Based on an Adaptive Hybrid Dynamic Stepsize and Target Attractive Force-RRT Algorithm

“…To address a vehicle's path planning, many kinds of path-planning algorithms have been proposed by researchers [10]. Zeng et al presented an online dynamic path re-planning system for an autonomous underwater vehicle [11]. Aghababa applied a numerical solution of the nonlinear optimal control problem (NOCP) to determine optimal paths in environments with obstacles [12].…”