Autonomous Underwater Vehicle Path Research Articles

Path Planning for Autonomous Underwater Vehicles With Simultaneous Arrival in Ocean Environment

This article focuses on the time-minimum path planning problem of autonomous underwater vehicles (AUVs) with the constraint of simultaneous arrival in an ocean environment. The overall framework can be divided into two successive modules, and both of them are solved by the biased min-consensus (BMC) method, which introduces the edge weight into the standard min-consensus protocol. The first module plans each AUV's path under the BMC protocol, respectively, along which the AUV will avoid obstacles and navigate to the destination with the minimum time through the ocean current. Incorporating the cooperative constraint of simultaneous arrival, the second part chooses the AUV path with the maximal voyage time as a benchmark, and replans other AUVs’ paths using the BMC method until their corresponding voyage time is approximately equal to the benchmark. The simulation results in various scenarios demonstrate the strong applicability and high effectiveness of our method.

ACO-A*: Ant Colony Optimization Plus A* for 3-D Traveling in Environments With Dense Obstacles

Path planning is one of the most important problems in the development of autonomous underwater vehicles (AUVs). In some common AUV missions, e.g., wreckage search for rescue, an AUV is often required to traverse multiple targets in a complex environment with dense obstacles. In such case, the AUV path planning problem becomes even more challenging. In order to address the problem, this paper develops a two-layer algorithm, namely ACO-A*, by combining the ant colony optimization (ACO) with the A* search. Once a mission with a set of arbitrary targets is assigned, ACO is responsible to determine the traveling order of targets. But, prior to ACO, a cost graph indicating the necessary traveling costs among targets must be quickly established to facilitate traveling order evaluation. For this purpose, a coarse-grained modeling with a representative-based estimation (RBE) strategy is proposed. Following the order obtained by ACO, targets will be traversed one by one and the pairwise path planning to reach each target can be performed during vehicle driving. To deal with the dense obstacles, A* is adopted to plan paths based on a fine-grained modeling and an admissible heuristic function is designed for A* to guarantee its optimality. Experiments on both synthetic and realistic scenarios have been designed to validate the efficiency of the proposed ACO-A*, as well as the effectiveness of RBE and the necessity of A*.

A Predictive Guidance Obstacle Avoidance Algorithm for AUV in Unknown Environments.

A predictive guidance obstacle avoidance algorithm (PGOA) in unknown environments is proposed for autonomous underwater vehicle (AUV) that must adapt to multiple complex obstacle environments. Using the environmental information collected by the Forward-looking Sonar (FLS), the obstacle boundary is simplified by the convex algorithm and Bessel interpolation. Combining the predictive control secondary optimization function and the obstacle avoidance weight function, the predicting obstacle avoidance trajectory parameters are obtained. According to different types of obstacle environments, the corresponding obstacle avoidance rules are formulated. Lastly, combining with the obstacle avoidance parameters and rules, the AUV’s predicting obstacle avoidance trajectory point is obtained. Then AUV can successfully achieve obstacle avoidance using the guidance algorithm. The simulation results show that the PGOA algorithm can better predict the trajectory point of the obstacle avoidance path of AUV, and the secondary optimization function can successfully achieve collision avoidance for different complex obstacle environments. Lastly, comparing the execution efficiency and cost of different algorithms, which deal with various complex obstacle environments, simulation experiment results indicate the high efficiency and great adaptability of the proposed algorithm.

Development of AUV path planner based on unstable mode

In this article, development of an autonomous underwater vehicle (AUV) path planner based on unstable mode is considered. The path planner is based on the bionic approach and does not require pre-mapping, it reduces requirements for the sensor subsystem of AUV. In the path planner, we use a method for obstacle avoidance in which an unstable mode is realized in a separate dynamic link. The output of the dynamic link corrects the desired angles of the AUV orientation. It allows setting the unstable mode only at the planning level, and at the regulatory level, a control system works in a stable mode. In addition, this approach allows planning the direction of obstacle avoidance. Detailed description of the proposed path planner and results of the study in Matlab are provided.

Path planning for autonomous underwater vehicle in time‐varying current

Due to the complexity of underwater environment, the navigation of autonomous underwater vehicle (AUV) is affected by current. Although some traditional methods can plan the non-collision path for AUV to reach the destination, it cannot overcome the influence of current on AUV navigation. In order to solve this problem, a velocity vector synthesis method combining belief function (BF) method is proposed. Among them, the BF method is used to dynamically plan the non-collision path. The velocity vector synthesis method is used to adjust the AUVs movement direction to offset the effect of time-varying current. The simulation results show that the proposed algorithm is effective and can overcome the problem of ‘current influence’. At the same time, compared with other methods, the proposed algorithm can guide AUV to achieve more efficient and adaptive path planning.

An energy-efficient data collection protocol with AUV path planning in the Internet of Underwater Things

Internet of Underwater Things (IoUT) is primarily comprised of acoustic sensor nodes, which are mostly battery-powered. The battery life of sensors is limited and it is inconvenient to recharge. However, these sensor nodes form the Underwater Wireless Sensor Networks (UWSNs), which can be clustered to save a massive amount of energy. Nevertheless, the selection of cluster head (CH) is a key issue as CH has to spend additional energy for performing data collection and aggregation before transmitting the data towards the destination. On the other hand, autonomous underwater vehicles (AUVs) are recommended to bridge UWSN with surface devices or internet. Yet, finding a path AUV should follow is an open issue. In this work, we proposed a protocol that utilizes AUV to save energy wasted by sensor nodes during clustering. To be precise, AUV not only collects data, but it is also responsible for nominating the Cluster Head (CH) and creating a wakeup-sleep schedule for clusters. In addition, we present AUV traversal path that is aware of residual energy in the network. Finally, an extensive quantitative evaluation of the proposed scheme against state of the art solutions is given.

Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming

For the complete coverage path planning of autonomous underwater vehicles (AUVs), a new strategy with Glasius bio-inspired neural network (GBNN) algorithm with discrete and centralized programming is proposed. The basic modeling for multi-AUVs complete coverage problem based on grid map and neural network is discussed first. Then, the design for single AUV complete coverage is introduced based on GBNN algorithm which is a new developed tool with small amount of calculation and high efficiency. In order to solve the difficulty of single AUV full coverage task of large water range, the multi-AUV full coverage discrete and centralized programming is proposed based on GBNN algorithm. The simulation experiment is conducted to confirm that through the proposed algorithm, multi-AUVs can plan reasonable and collision-free coverage path and reach full coverage on the same task area with division of labor and cooperation.

Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

This paper presents an autonomous underwater vehicle (AUV) path planning scenario as an optimization problem constrained by the combination of hard constraints and soft constraints. The path planner aims to generate the optimum path that safely guides an AUV through an ocean environment with priori known obstacles and non-uniform currents in both 2D and 3D. The path planner uses 2 variants of particle swarm optimization (PSO) algorithms, which are the selectively Differential Evolution (DE)-hybridized Quantum PSO (SDEQPSO) and Adaptive PSO (SDEAPSO). The performances of the path planners using different constraints are analyzed in a series of extensive Monte Carlo simulations and ANOVA (analysis of variance) procedures based on their respective solution qualities, stabilities and computational efficiencies. Based on the simulation results, the SDEQPSO path planner with the setting of hard constraint for boundary condition and soft constraint for obstacle avoidance was found to be able to generate smooth and feasible AUV path with higher efficiency than other algorithms, as indicated by its relatively low computational requirement and excellent solution quality.

Cooperative Navigation Based on Cross Entropy: Dual Leaders

Cooperative navigation aims at improving positioning accuracy of Autonomous Underwater Vehicles (AUVs). In this paper, a dual leaders cooperative navigation method is proposed based on Cross Entropy (CE) algorithm. Since the trajectories of the slave AUVs are assumed to be predetermined, the Markov Decision Process (MDP) is also integrated in the proposed algorithm to generate optimal trajectories of master AUVs from the perspective of probability. Firstly, the navigation model and cost functions are established for the cooperative navigation system with multiple masters and slaves. Then, the CE algorithm is used to train the system with help of MDP to obtain the path of the master AUVs. In the simulation, the cooperative localization trajectories of the slave AUVs are obtained by Extended Kalman Filter (EKF) and are compared with other positioning methods. The results show that the trajectories of dual master AUVs obtained by the proposed algorithm can not only reduce the observation error of the slave AUVs in the system effectively, but also keep relative measurement distance between the master AUVs and the slave AUVs in a suitable range.

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research.

Intelligent Path Planning for AUVs in Dynamic Environments: An EDA-Based Learning Fixed Height Histogram Approach

Autonomous underwater vehicles (AUVs) are robots that require path planning to complete missions in different kinds of underwater environments. The goal of path planning is to find a feasible path from the start-point to the target-point in a given environment. In most practical applications, environments have dynamic factors, such as ocean flows and moving obstacles, which make the AUV path planning more challenging. This paper proposes an estimation of distribution algorithm (EDA) based approach, termed as learning fixed-height histogram (LFHH) to solve path planning problems for AUVs in dynamic environment. The LFHH uses a learning transformation strategy (LTS) to improve its accuracy and convergence speed. Besides, a smooth method is employed to accelerate the speed of finding feasible paths. Moreover, a planning window is adopted to help handle dynamic factors. LFHH is tested in both complex 2-D and 3-D environments with time-variant dynamic factors, and experimental results validate the effectiveness of LFHH.

A Real-Time Path Planning Algorithm for AUV in Unknown Underwater Environment Based on Combining PSO and Waypoint Guidance.

It is a challengeable task to plan multi-objective optimization paths for autonomous underwater vehicles (AUVs) in an unknown environments, which involves reducing travel time, shortening path length, keeping navigation safety, and smoothing trajectory. To address the above challenges, a real-time path planning approach combining particle swarm optimization and waypoint guidance is proposed for AUV in unknown oceanic environments in this paper. In this algorithm, a multi-beam forward looking sonar (FLS) is utilized to detect obstacles and the output data of FLS are used to produce those obstacles’ outlines (polygons). Particle swarm optimization is used to search for appropriate temporary waypoints, in which the optimization parameters of path planning are taken into account. Subsequently, an optimal path is automatically generated under the guidance of the destination and these temporary waypoints. Finally, three algorithms, including artificial potential field and genic algorithm, are adopted in the simulation experiments. The simulation results show that the proposed algorithm can generate the optimal paths compared with the other two algorithms.

Data-Gathering Protocol-Based AUV Path-Planning for Long-Duration Cooperation in Underwater Acoustic Sensor Networks

In recent years, as autonomous underwater vehicles (AUVs) have been widely used to prolong the lifetime of underwater acoustic sensor networks (UWASNs) through cooperative data-gathering with sensor nodes, the need for long-range AUVs has become more pronounced to support long-duration cooperation with sensor nodes. Hence, AUV-based data-gathering protocols are required to support the long-duration cooperation by operating the long-range AUVs. However, the recently proposed AUV-based data-gathering protocols do not consider the energy consumption and tour time of AUVs, and what is worse repeat the cluster reconstruction by AUVs for the uniform energy consumption. Thus, via these protocols, AUVs can deplete their energy more quickly before fulfilling their missions. Also, there are path-planning algorithms that have reflected AUV’s energy consumption. However, they do not consider AUV’s maneuvering that causes AUVs to pay lots of costs, and thus AUVs cannot perform the long-range operation. The objective of this paper is to develop a data-gathering protocol based on AUV path planning that maximizes the mission time for the cooperative data-gathering. To achieve this goal, an enhanced lawn mower pattern path is designed to support both the long-range AUVs and the uniform energy consumption of sensor nodes. Simulation results show that the proposed protocol outperforms the recently proposed data-gathering protocol with respect to the long-duration cooperation for data-gathering, thereby increasing the lifetime of UWASNs.

Inversion of range-dependent ocean environmental parameters using radiation noises of an Autonomous Underwater Vehicle

Moving platform is playing an increasingly important role in ocean observatories. We previously reported some field testing results of using the radiation noises of an Autonomous Underwater Vehicle (AUV) as the sound source to invert the range-independent SSP. To incorporate source motion effects, the forward model based on the waveguide Doppler and normal mode theory was applied to compute the replica field, and to resolve the adjacent Doppler shifted frequencies, an analytical solution of the forward model and its simplified version were obtained for arbitrary signal integration intervals with a monochromatic source. In this paper, we reformulate the matched-field inversion problem into a state-space model to track the range-dependent environmental parameters and moving source parameters along the AUV path with the constantly updated measurement equations. Performances of several sequential filters, including extended Kalman, unscented Kalman, and particle filters, are compared. Note that at each point of the track, the estimate is the average from the source to the receiver; by doing the inversion sequentially with AUV moving, one can get the parameter estimate at individual points (sections). This process is equivalent to iteration over space, while iteration over time is considered as a solution to an under-determined inverse problem.Moving platform is playing an increasingly important role in ocean observatories. We previously reported some field testing results of using the radiation noises of an Autonomous Underwater Vehicle (AUV) as the sound source to invert the range-independent SSP. To incorporate source motion effects, the forward model based on the waveguide Doppler and normal mode theory was applied to compute the replica field, and to resolve the adjacent Doppler shifted frequencies, an analytical solution of the forward model and its simplified version were obtained for arbitrary signal integration intervals with a monochromatic source. In this paper, we reformulate the matched-field inversion problem into a state-space model to track the range-dependent environmental parameters and moving source parameters along the AUV path with the constantly updated measurement equations. Performances of several sequential filters, including extended Kalman, unscented Kalman, and particle filters, are compared. Note that at each point ...

Biologically Inspired Self-Organizing Map Applied to Task Assignment and Path Planning of an AUV System

An integrated biologically inspired self-organizing map (SOM) algorithm is proposed for task assignment and path planning of an autonomous underwater vehicle (AUV) system in 3-D underwater environments with obstacle avoidance. The algorithm embeds the biologically inspired neural network (BINN) into the SOM neural networks. The task assignment and path planning aim to arrange a team of AUVs to visit all appointed target locations, while assuring obstacle avoidance without speed jump. The SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in underwater environments. Then, in order to avoid obstacles and speed jump for each AUV that visits the corresponding target location, the BINN is utilized to update weights of the winner of SOM, and achieve AUVs path planning and effective navigation. The effectiveness of the proposed hybrid model is validated by simulation studies.

Data Collection Strategy for Magnetic Induction Based Monitoring in Underwater Sensor Networks

Underwater wireless sensor networks (UWSNs) based on magnetic induction (MI) have been recently proposed as a promising candidate for underwater networking due to its benefits, such as small transmission delay, low vulnerability to environment changes, multipath fading negligibility, and high bandwidth. Most of the UWSN applications are location dependent and, thus, localization plays an important functionality for obtaining sensor positions. In this paper, we first study an MI-based monitoring network in shallow sea, then focus on how to design an optimal node deployment strategy and a clustering algorithm to prolong network lifetime for a 3D-UWSN by reducing the network energy consumption. Using the Voronoi diagram, we propose a high-energy node priority clustering algorithm, in which a cluster head would be selected according to the remaining energy of sensor nodes and the geometry distance among them. Moreover, in order to improve the efficiency of data collection, we use the ant colony optimization to find the shortest path for autonomous underwater vehicle. The simulation results show that the proposed approach outperforms other conventional protocols in some certain scenarios.

An Optimized Fuzzy Control Algorithm for Three-Dimensional AUV Path Planning

Path planning is an important issue for autonomous underwater vehicle (AUV) underwater mission. In this paper, an optimized fuzzy control algorithm is proposed for three-dimensional (3D) AUV path planning. Based on the sonar model, it can be used for path planning in the complex underwater environment. First, the path planning is established based on the two sonars arranged on the horizontal plane and vertical plane. On the environment information collected by sonars, the virtual acceleration and velocity of AUV in the three-dimensional space can be obtained through the fuzzy system with accelerate/break module which enables AUV to avoid dynamic obstacles automatically. Considering the fuzzy boundary choice has great subjectivity, the generated path cannot guarantee to be optimal. Therefore, in order to deal with this problem, two optimized methods are compared to do the optimization of the fuzzy set. Simulation results indicate that the proposed method can generate an optimal 3D path under three-dimensional underwater environment and can be used in real case in the future.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

A comparison of optimization techniques for AUV path planning in environments with ocean currents

To date, a large number of optimization algorithms have been presented for Autonomous Underwater Vehicle (AUV) path planning. However, little effort has been devoted to compare these techniques. In this paper, an quantum-behaved particle swarm optimization (QPSO) algorithm is introduced for solving the optimal path planning problem of an AUV operating in environments with ocean currents. An extensive study of the most important optimization techniques applied to optimize the trajectory for an AUV in several test scenarios is presented. Extensive Monte Carlo trials were also run to analyse the performance of these optimization techniques based on solution quality and stability. The weaknesses and strengths of each technique have been stated and the most appropriate algorithm for AUV path planning has been determined.

H∞ control for path tracking of autonomous underwater vehicle motion

In order to simplify the design of path tracking controller and solve the problem relating to nonlinear dynamic model of autonomous underwater vehicle motion planning, feedback linearization method is first adopted to transform the nonlinear dynamic model into an equivalent pseudo-linear dynamic model in horizontal coordinates. Then considering wave disturbance effect, mixed-sensitivity method of H∞ robust control is applied to design state-feedback controller for this equivalent dynamic model. Finally, control law of pseudo-linear dynamic model is transformed into state (surge velocity and yaw angular rate) tracking control law of nonlinear dynamic model through inverse coordinate transformation. Simulation indicates that autonomous underwater vehicle path tracking is successfully implemented with this proposed method, and the influence of parameter variation in autonomous underwater vehicle dynamic model on its tracking performance is reduced by H∞ controller. All the results show that the method proposed in this article is effective and feasible.