Efficient collision-free path planning for autonomous underwater vehicles in dynamic environments with a hybrid optimization algorithm

publisher: Elsevier articletitle: Efficient collision-free path planning for autonomous underwater vehicles in dynamic environments with a hybrid optimization algorithm journaltitle: Ocean Engineering articlelink: http://dx.doi.org/10.1016/j.oceaneng.2016.09.040 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved

Path planning methods for AUVs

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 75-81).From naval operations to ocean science missions, the importance of autonomous vehicles is increasing with the advances in underwater robotics technology. Due to the dynamic and intermittent underwater environment and the physical limitations of autonomous underwater vehicles, feasible and optimal path planning is crucial for autonomous underwater operations. The objective of this thesis is to develop and demonstrate an efficient underwater path planning algorithm based on the level set method. Specifically, the goal is to compute the paths of autonomous vehicles which minimize travel time in the presence of ocean currents. The approach is to either utilize or avoid any type of ocean flows, while allowing for currents that are much larger than the nominal vehicle speed and for three-dimensional currents which vary with time. Existing path planning methods for the fields of ocean science and robotics are first reviewed, and the advantages and disadvantages of each are discussed. The underpinnings of the level set and fast marching methods are then reviewed, including their new extension and application to underwater path planning. Finally, a new feasible and optimal time-dependent underwater path planning algorithm is derived and presented. In order to demonstrate the capabilities of the algorithm, a set of idealized test-cases of increasing complexity are first presented and discussed. A real three-dimensional path planning example, involving strong current conditions, is also illustrated. This example utilizes four-dimensional ocean flows from a realistic ocean prediction system which simulate the ocean response to the passage of a tropical storm in the Middle Atlantic Bight region.by Konuralp Yiğit.S.M

Optimal trajectory generation in ocean flows

In this paper it is shown that Lagrangian Coherent Structures (LCS) are useful in determining near optimal trajectories for autonomous underwater gliders in a dynamic ocean environment. This opens the opportunity for optimal path planning of autonomous underwater vehicles by studying the global flow geometry via dynamical systems methods. Optimal glider paths were computed for a 2-dimensional kinematic model of an end-point glider problem. Numerical solutions to the optimal control problem were obtained using Nonlinear Trajectory Generation (NTG) software. The resulting solution is compared to corresponding results on LCS obtained using the Direct Lyapunov Exponent method. The velocity data used for these computations was obtained from measurements taken in August, 2000, by HF-Radar stations located around Monterey Bay, CA

Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning

Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.785

Assessment of Collision Avoidance Strategies for an Underwater Transportation System

Transportation using multiple autonomous vehicles with detection avoidance capability is useful for military applications. It is important for such systems to avoid collisions with underwater obstacles in an effective way, while keeping track of the target location. In this paper, sensor-based and path-planning methods of external collision avoidance were investigated for an underwater transportation system. In particular, sensor-based wall-following and hard-switching collision avoidance strategies and an offline RRT* path-planning method was implemented on the simulation model of the transportation system of four Hovering Autonomous Underwater Vehicles (HAUVs). Time-domain motion simulations were performed with each method and their ability to avoid obstacles was compared. The hard-switching method resulted in high yaw moments which caused the vehicle to travel towards the goal by a longer distance. Conversely, in the wall-following method, the yaw moment was kept to zero. Moreover, the wall-following method was found to be better than the hard-switching method in terms of time and power efficiency. The comparison between the offline RRT* path-planning and wall-following methods showed that the fuel efficiency of the former is higher whilst its time efficiency is poorer. The major drawback of RRT* is that it can only avoid the previously known obstacles. In future, offline RRT* and wall following can be blended for a better solution. The outcome of this paper provides guidance for the selection of the most appropriate method for collision avoidance for an underwater transportation system

Exact and heuristic algorithms for multi-robot system routing, oriented to underwater monitoring. ​

The exploration of the underwater environment has always been a relevant field for science and technology, to enlarge our knowledge of this mainly unexplored environment. In this work, we apply a vehicle routing optimization method for underwater exploration and monitoring based on a fleet of small autonomous underwater vehicles (AUVs). We assume a coarse-grained map is already available from satellite measurements and the set of robots is used to get detailed information on sea bottom features. We provide exact and heuristic linear programming methods for finding both the optimal starting position and path planning for a fleet of drones. To obtain a realistic model useful in real applications, we enhance our formulation by imposing connectivity constraints among the AUVs. Lastly, we present a use case application for coral reef monitoring with real data taken by Abu Dhabi environmental authorities.The exploration of the underwater environment has always been a relevant field for science and technology, to enlarge our knowledge of this mainly unexplored environment. In this work, we apply a vehicle routing optimization method for underwater exploration and monitoring based on a fleet of small autonomous underwater vehicles (AUVs). We assume a coarse-grained map is already available from satellite measurements and the set of robots is used to get detailed information on sea bottom features. We provide exact and heuristic linear programming methods for finding both the optimal starting position and path planning for a fleet of drones. To obtain a realistic model useful in real applications, we enhance our formulation by imposing connectivity constraints among the AUVs. Lastly, we present a use case application for coral reef monitoring with real data taken by Abu Dhabi environmental authorities