Autonomous Underwater Vehicle Path Planning Research Articles

AUV path planning in a three-dimensional marine environment based on a novel multiple swarm co-evolutionary algorithm

Autonomous underwater vehicles (AUVs) are rapidly advancing in ocean exploration. High-performance path planning techniques are essential for AUVs. Path planning for AUVs is a multi-faceted challenge, necessitating careful consideration of safety, energy consumption and the influence of sea currents to ensure the development of a high-quality trajectory that satisfies all operational criteria. Existing path planning algorithms have problems such as incomplete consideration of influencing factors, computational complexity and weak applicability. Therefore, we propose a swarm intelligence optimisation algorithm based on multiple swarm co-evolution (MCO) to address the issue of AUV path planning in a three-dimensional marine environment. First, a three-dimensional marine environment model and the corresponding path evaluation mechanism are established. Second, the MCO rule is established to ensure that the MCO has balanced exploration and exploitation capabilities. A shared dynamic optimal particle between populations is introduced to ensure information exchange between populations. In addition, the cross-integration mutation strategy has been proposed for promoting the fusion of two populations of superior genes to ensure the inheritance of superior paternal genes to the offspring. Finally, four comparison experiments are designed, and the experiments compare eight commonly used intelligent search algorithms and improved versions. The results of the experiments proved that the MCO has excellent three-dimensional marine environment path planning capability, with robustness and search capabilities superior to other swarm intelligence optimisation algorithms.

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Path planning of autonomous underwater vehicle in unknown environment based on improved deep reinforcement learning

In order to improve the autonomous navigation capability of autonomous underwater vehicles (AUV), this paper applies Deep Reinforcement Learning (DRL) to realize autonomous path planning. In response to the limited detection range and difficult tracking DRL policy in three-dimensional unknown environments, this paper proposes an improved deep reinforcement learning method. Firstly, performing AUV modeling and processing obstacle detection information. Second, under a framework of reinforcement learning with the control system, an improved environmental interaction method was designed by analyzing the motion characteristics and controller performance of the AUV. Finally, based on this method, a policy network that can generate paths that satisfy obstacle avoidance and navigation is trained in combination with a twin delayed deterministic policy gradient (TD3). The simulation results show that the proposed method can achieve AUV path planning in a three-dimensional unknown environment. The proposed framework can ensure that the planned path meets the dynamics constraints and is trackable. The improved algorithm can enhance the convergence of training and the attitude stability of AUV.

A Cooperative Evolutionary Computation Algorithm for Dynamic Multiobjective Multi-AUV Path Planning

Multiple autonomous underwater vehicles (AUVs) are popular for executing submarine missions, which involve multiple targets distributed in a large and complex underwater environment. The path planning of multiple AUVs is a significant and challenging problem, which determines the location of surface points for AUV launch and plans the paths of AUVs for target traveling. Most existing works model the problem as a single-objective static optimization problem. However, the target missions may change over time, and multiple optimization objectives are usually expected for decision making. Thus, this paper models the problem as a dynamic multiobjective optimization problem and proposes a cooperative evolutionary computation algorithm to provide diverse and high-quality solutions for decision makers. In the proposed method, solutions are represented using a bi-layer encode scheme, in which the first layer indicates the surface location points and the second layer represents the traveling sequences of target missions. Multiple populations for multiple objectives (MPMO) framework is adopted to efficiently solve the dynamic multiobjective AUV optimization problem. In addition, a recombination-based sampling strategy is developed to improve convergence by fusing the information of multiple populations. Once a change occurs, an incremental response strategy is adopted to generate high-quality solutions for population evolution. Based on the dataset of New Zealand bathymetry, six complex underwater scenarios are constructed with a size of 50 km×50 km×10 km and 400 target missions for tests. Experimental results show that the proposed method outperforms the state-of-the-art algorithms in terms of solution diversity and optimality.

Technical Design and Data Analysis of Autonomous Underwater Vehicle-Based Side-Scan Sonar Operations

In response to the absence of standardized work practices, work safety measures, efficient work procedures, and suitable line planning methods for exploring seabed topography using autonomous underwater vehicles (AUVs) equipped with side-scan sonar systems, this paper proposes a design framework for side-scan sonar measurement technology based on AUVs. Detailed research has been conducted on line planning methods that differ significantly from those used in shipborne operations. The new line planning method was developed based on the performance characteristics of AUV equipment, the principles of motion modes, and shipborne side-scan sonar operations. The validity of the line planning method was verified through actual sea experiments using different types of AUVs in various terrains. The experimental results demonstrate that the new survey line planning method is distinct from existing AUV path planning methods and is highly applicable for conducting seabed topography exploration operations. Furthermore, the technical design scheme studied in this article is applicable in different sea areas and ensures the safety, operational efficiency, and data quality of AUVs. It holds significant importance for marine science engineering and practical applications. The article also features an analysis of successful data collected from AUVs equipped with side-scan sonar for conducting seabed topography exploration operations.

A conflict-directed approach to chance-constrained mixed logical linear programming

Resistance to the adoption of autonomous systems comes in part from the perceived unreliability of the systems. The concerns can be addressed by deploying decision making algorithms that defines what it means to fail, and look for plans with the highest reward while limiting the probability of failure.This chance-constrained approach thus explicitly imposes a set of constraints that must be satisfied for success, and provides upper-bounds on the probability of violating such constraints. A chance-constrained mixed logical-linear program (CC-MLLP) is a natural formulation, allowing for the specification of linear and logical constraints, with probabilistic continuous variables. The formalism can be used to describe problems ranging from autonomous underwater vehicle path planning, to network routing under uncertainty.While naive encodings of CC-MLLPs can be solved with generalised solvers, the solution time may be unreasonable. In this work, we study architectures to speed up solutions by partitioning CC-MLLPs into the discrete and continuous portions. In order to provide faster solutions, we investigate methods for speeding up the solutions to the continuous chance-constrained linear programs. Further, by exploiting the new solution methods, we develop techniques for guiding the discrete decision making portion of the problem. The resulting algorithm achieves 10 times speed up over prior approaches on autonomous path planning benchmarks.

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning.

With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs to conduct underwater exploration missions. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. In this paper, we propose a method to improve the local minimum in the artificial potential field (APF) to make AUVs out of the local minimum by constructing a traction force. The improved artificial potential field (IAPF) method is combined with DRL for path planning while optimizing the reward function in the DRL algorithm and using the generated path to optimize the future path. By comparing our results with the experimental data of various algorithms, we found that the proposed method has positive effects and advantages in path planning. It is an efficient and safe path-planning method with obvious potential in underwater navigation devices.

Data collection optimization of ocean observation network based on AUV path planning and communication

The Ocean Observation Network (OON) is an ocean science and engineering observation platform which integrates underwater wireless sensors, acoustic buoys, submersible buoys, and sonars. The complex underwater environment makes it challenging to place the observation nodes flexibly. In this research, firstly, a novel OON data collection optimization scheme is proposed, which is based on Autonomous Underwater Vehicle (AUV) path planning and communication. By combining the Voronoi diagram and K-means algorithm, an optimized clustering algorithm (VorK-means) is proposed to optimize the location of junction boxes and reduce the losses in the transmission energy of submarine cables in the OON simultaneously. Subsequently, a computational algorithm for AUV data collection position is proposed based on Voronoi vertices and communication circles. Moreover, an improved Grey Wolf Optimization (GWO) algorithm, based on the Levy flight and dimension (LD) search strategy, is proposed and applied for the AUV path planning, which is named LD-GWO. The accuracy and efficiency are validated by applying the proposed algorithms to the Canadian North-East Pacific Time-series Underwater Networked Experiment (NEPTUNE). Compared with the K-means algorithm, the VorK-means algorithm can reduce the total length of submarine cables by nearly 14%, and compared with the GWO algorithm, LD-GWO can obtain shorter data collection paths under identical parameter settings.

Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles

This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (GA) of bionic-inspired algorithms are integrated to implement a hybrid grey wolf optimization (HGWO) algorithm which allows AUVs to reach their destination safely in an obstacle rich environment. The proposed hybrid path planner is employed for path planning of a single AUV based on collision avoidance. It uses the GA as an initialization generator to overcome the random initialization problem of GWO. In this research, the total cost is considered to be a function of path distance and collision penalties. Further, the application of the proposed hybrid path planner is extended for cooperative path planning of AUVs while avoiding collision using communication consensus. Simulation results are obtained for both a single AUV and multiple AUV path planning in a 3D obstacle rich environment using a proportional-derivative controller. The Kruskal–Wallis test is employed for a non-parametric statistical analysis, where the independence of the results given by the algorithms is demonstrated.

Three-Dimensional Underwater Path Planning Based on Modified Potential Field Algorithm in Time-Varying Current

Abstract The article addresses the three-dimensional (3D) underwater path planning problem of an autonomous underwater vehicle (AUV) in a time-varying current. A modified artificial potential field algorithm combining the velocity vector synthesis method is proposed to search for the optimal path. The modified potential field (MPF) algorithm is designed to dynamically plan the non-collision path. Meanwhile, this modified method is also proved to be an effective solution to the goals not reachable with obstacles nearby (GNRON), U-shaped trap, and rotation unreachable problems. To offset the influence of time-varying current, the velocity synthesis approach is designed to adjust the AUV movement direction. Besides, considering path planning in the complex underwater environment, the multi-beam forward-looking sonar (FLS) model is used. Finally, simulation studies substantiate that the designed algorithm can implement the AUV path planning effectively and successfully in a 3D underwater environment.

A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning

Path planning technology is an important guarantee for the safe navigation of autonomous underwater vehicle (AUV) in water, and it is also an important indicator of the intelligence of autonomous underwater vehicle. Aiming at the path planning problem of AUV in complex environments, this article presents a reinforcement learning-based tuna swarm optimization algorithm called the QLTSO. In this algorithm, individuals are independent of each other, and the choice of each individual strategy is decided by reinforcement learning. Four strategies are set for each individual in the algorithm: spiral foraging, parabolic foraging, optimization adjustment and ESOS strategy. Finally, the cubic B-spline curve is used to smooth the path so that the autonomous underwater vehicle can better track the path. To verify the superiority of the QLTSO algorithm, the algorithm is compared with other advanced optimization algorithms. The simulation results show that the QLTSO algorithm can plan safe and effective AUV navigation paths in a variety of two-dimensional and three-dimensional complex environments with better convergence and robustness, and the planning success rate is up to 100%, which is an effective AUV path planning algorithm.

A Time-Saving Path Planning Scheme for Autonomous Underwater Vehicles With Complex Underwater Conditions

Autonomous underwater vehicle (AUV) shows great potential in the Internet of Underwater Things (IoUT) system, in which the path planning algorithm plays a fundamental role. However, the complex underwater environment brings greater challenges to AUV path planning, especially the ocean current, which has a profound impact on time and energy consumption. This article focuses on the complex ocean current condition and proposes an underwater path planning method based on proximal policy optimization (UP4O). In this novel method, a deep reinforcement network is constructed to serve as a decision control to plan the moving direction of AUV. An information encoding module is developed to extract the features of the local obstacles. Furthermore, UP4O integrates the obstacle features with the current state information, including relative position, ocean current, and velocity, enabling the AUV to focus on the global direction and local obstacles at the same time. Additionally, to further adapt to the ocean current and shorten the time cost, UP4O expands the action space of AUV, realizing a fine and flexible action adjustment. The wide applicability of UP4O has been proved by numerous experiments. The proposed algorithm can always plan the time-saving and collision-free paths in complex underwater environments with various terrains and ocean current.

Autonomous Underwater Vehicle Path Planning Method of Soft Actor–Critic Based on Game Training

This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwater navigation. A game-based soft actor–critic (GSAC) path planning method is proposed in this study to improve the adaptive capability of autonomous planning and the reliability of obstacle avoidance in the unknown underwater environment. Considering the influence of the simulation environment, the obstacles in the simulation environment are regarded as agents and play a zero-sum game with the AUV. The zero-sum game problem is solved by improving the strategy of AUV and obstacles, so that the simulation environment evolves intelligently with the AUV path planning strategy. The proposed method increases the complexity and diversity of the simulation environment, enables AUV to train in a variable environment specific to its strategy, and improves the adaptability and convergence speed of AUV in unknown underwater environments. Finally, the Python language is applied to write an unknown underwater simulation environment for the AUV simulation testing. GSAC can guide the AUV to the target point in the unknown underwater environment while avoiding large and small static obstacles, canyons, and small dynamic obstacles. Compared with the soft actor–critic(SAC) and the deep Q-network (DQN) algorithm, GSAC has better adaptability and convergence speed in the unknown underwater environment. The experiments verifies that GSAC has faster convergence, better stability, and robustness in unknown underwater environments.

Risk-based path planning for autonomous underwater vehicles in an oil spill environment

Autonomous underwater vehicles (AUVs) are advanced platforms for detecting and mapping oil spills in deep water. However, their applications in complex spill environments have been hindered by the risk of vehicle loss. Path planning for AUVs is an effective technique for mitigating such risks and ensuring safer routing. Yet previous studies did not address path searching problems for AUVs based on probabilistic risk reasoning. This study aims to propose an offboard risk-based path planning approach for AUVs operating in an oil spill environment. A risk model based on the Bayesian network was developed for probabilistic reasoning of risk states given varied environmental observations. This risk model further assisted in generating a spatially-distributed risk map covering a potential mission area. An A*-based searching algorithm was then employed to plan an optimal-risk path through the constructed risk map. The proposed planner was applied in a case study with a Slocum G1 Glider in a real-world spill environment around Baffin Bay. Simulation results proved that the optimal-risk planner outperforms in risk mitigation while achieving competitive path lengths and mission efficiency. The proposed method is not constrained to AUVs but can be adapted to other marine robotic systems.

Path Planning of AUV Swarms Using a Bio-inspired Multi-Agent System

This paper uses a multi-agent system (MAS) as the bioinformatics-inspired technique for guiding a team of autonomous underwater vehicles (AUVs) toward the desired destination. An individual AUV is designated as an agent connected by a communication network and assumes full communication. Here, each AUV estimates the position of its neighbor AUVs while moving toward the destination. The proposed multi-AUV system consists of a leader AUV and five follower AUVs. A distributed path consensus (DPC) is proposed to ensure the neighboring agent AUVs maintain a predefined distance between each other while moving toward the predefined destination. Due to the proposed distance constraint between neighboring agents, AUVs stay at a safe distance from each other while maintaining underwater communication using interactive switching topology. The performance of the optimized path is obtained using MATLAB simulation. The proposed algorithm is applied in both, formation using the desired shape, and trajectory tracking, and found to be globally asymptotically stable. The results of the simulation confirm that each agent switches from one state to another and progress over time until the desired coordinated shape is achieved without inter-vehicular collision. The proposed method solves coordination problems among multiple AUVs and increases the coverage of underwater missions like oceanographic surveys.

Power-Efficient Data Collection Scheme for AUV-Assisted Magnetic Induction and Acoustic Hybrid Internet of Underwater Things

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

Efficient time-optimal path planning of AUV under the ocean currents based on graph and clustering strategy

Path planning is particularly important for Autonomous Underwater Vehicles (AUVs) in the marine environments. Due to the existence of flow fields, many conventional path planning methods are not effective. As the flow distributions vary slowly in large spatial scales, this paper considers the influence of static non-uniform vector field and studies the time-optimal path planning of AUV. The method proposed in this paper is called GK-OPM (Graph and K-means based Optimization Method). GK-OPM generates initial paths for minimum-time path optimization using digraph and K-means method, which benefit optimality and efficiency, respectively. To discuss the parameters in GK-OPM and compare the performance with genetic algorithm (GA) and particle swarm optimization (PSO), Monte Carlo simulation is carried out under the ocean currents simulated by the Lamb-vertex function. The feasibility is verified on discrete ocean data and three-dimensional environments, too. The results show that GK-OPM has about 6–8 times better efficiency than GA and PSO and maintains optimality at the same time.

Numerical Simulation of Time-Optimal Path Planning for Autonomous Underwater Vehicles Using a Markov Decision Process Method

Many path planning algorithms developed for land or air based autonomous vehicles no longer apply under the water. A time-optimal path planning method for autonomous underwater vehicles (AUVs), based on a Markov decision process (MDP) algorithm, is proposed for the marine environment. Its performance is examined for different oceanic conditions, including complex coastal bathymetry and time-varying ocean currents, revealing advantages compared to the A* algorithm, a traditional path planning method. The ocean current is predicted using a regional ocean model and then provided to the MDP algorithm as a priori. A computation-efficient and feature-resolved spatial resolution are determined through a series of sensitivity experiments. The simulations demonstrate the importance to incorporate ocean currents in the path planning of AUVs in the real ocean. The MDP algorithm remains robust even if the ocean current is complex.

Multi-AUV Collaborative Data Collection Algorithm Based on Q-Learning in Underwater Acoustic Sensor Networks

Intelligent data collection is a key component in underwater acoustic sensor networks, and it plays an important role in seabed environment monitoring, marine resource detection and marine disaster early warning. Owing to the particularities of the underwater environment, such as reduced infrastructure and noisy communication channels, the data collected by underwater nodes are more efficiently transmitted to a control center on the surface by way of an autonomous underwater vehicle (AUV). However, with the increasing complexity of the underwater tasks, using a single AUV for data collection cannot meet the requirements of low latency and low power consumption. To solve this problem, a multi-AUV collaborative data collection algorithm that reduces the load of data collection task on a single AUV is proposed. The algorithm is divided into two stages: multi-AUV task allocation and Q-learning-based AUV path planning. The data transmission of the clusters is regarded as a set of different tasks, which are assigned to the AUVs for completion. Subsequently, path planning is performed to guide the AUVs, so that the tasks are completed promptly and at a reduced cost. Simulation results show that the proposed algorithm can leverage the energy consumption of a network and extend its lifetime. The performance of the proposed algorithm in energy consumption is increased by about 10%, and the delay of data collection is also significantly reduced.

Path Planning for Autonomous Underwater Vehicles Under the Influence of Ocean Currents Based on a Fusion Heuristic Algorithm

Recently, research on path planning for the autonomous underwater vehicles (AUVs) has developed rapidly. Heuristic algorithms have been widely used to plan a path for AUV, but most traditional heuristic algorithms are facing two problems, one is slow convergence speed, the other is premature convergence. To solve the above problems, this paper proposes a new heuristic algorithms fusion, which improves the genetic algorithm with the ant colony optimization algorithm and the simulated annealing algorithm. In addition, to accelerate convergence and expand the search space of the algorithm, some algorithms like trying to cross, path self-smoothing and probability of genetic operation adjust adaptively are proposed. The advantages of the proposed algorithm are reflected through simulated comparative experiments. Besides, this paper proposes an ocean current model and a kinematics model to solve the problem of AUV path planning under the influence of ocean currents.