Path Planning Of Underwater Vehicle Research Articles

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm

Underwater autonomous path planning is a critical component of intelligent underwater vehicle system design, especially for maritime conservation and monitoring missions. Effective path planning for these robots necessitates considering various constraints related to robot kinematics, optimization objectives, and other pertinent factors. Sample-based strategies have successfully tackled this problem, particularly the rapidly exploring random tree star (RRT*) algorithm. However, conventional path-searching algorithms may face challenges in the marine environment due to unique terrain undulations, sparse and unpredictable obstacles, and inconsistent results across multiple planning iterations. To address these issues, we propose a new approach specifically tailored to the distinct features of the marine environment for navigation path planning of underwater vehicles, named bidirectional cached rapidly exploring random tree star (BCRRT*). By incorporating bidirectional path planning and caching algorithms on top of the RRT*, the search process can be expedited, and an efficient path connection can be achieved. When encountering new obstacles, ineffective portions of the cached path can be efficiently modified and severed, thus minimizing the computational workload while enhancing the algorithm’s adaptability. A certain number of simulation experiments were conducted, demonstrating that our proposed method outperformed cutting-edge techniques like the RRT* in several critical metrics such as the density of path nodes, planning time, and dynamic adaptability.

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.

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.

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.

Dynamic Path Planning of Underwater Vehicle Based on Pulse Lidar Profiler

In the underwater environment, the speed of light is much faster than that of sonar, so it can be used to sample and detect more intensively in space and high frequency in time. A better strategy is to integrate sonar and lidar into an integrated device. In military affairs, lidar sonar system is used for identification of underwater artificial works and fixed-point blasting. In the civil field, aquaculture, scientific research and sampling, and underwater infrastructure exploration are the main areas. This paper analyzes the application of pulse lidar profiler system integrated with multiple detection methods in the dynamic path planning of underwater vehicle. Three colors of “red, orange, red sauce” were used as the warm color light source group, and three colors of “blue, green, purple” were used as the cold color light source group. Through matrix operation, underwater objects with almost all colors can be quickly reached and superimposed. The speed of sound wave is slow, but the bypass performance is very good, which fully supplements the blind area of light source.

Autonomous underwater vehicle path planning based on actor-multi-critic reinforcement learning

The environmental adaptability of autonomous underwater vehicles is always a problem for its path planning. Although reinforcement learning can improve the environmental adaptability, the slow convergence of reinforcement learning is caused by multi-behavior coupling, so it is difficult for autonomous underwater vehicle to avoid moving obstacles. This article proposes a multi-behavior critic reinforcement learning algorithm applied to autonomous underwater vehicle path planning to overcome problems associated with oscillating amplitudes and low learning efficiency in the early stages of training which are common in traditional actor–critic algorithms. Behavior critic reinforcement learning assesses the actions of the actor from perspectives such as energy saving and security, combining these aspects into a whole evaluation of the actor. In this article, the policy gradient method is selected as the actor part, and the value function method is selected as the critic part. The strategy gradient and the value function methods for actor and critic, respectively, are approximated by a backpropagation neural network, the parameters of which are updated using the gradient descent method. The simulation results show that the method has the ability of optimizing learning in the environment and can improve learning efficiency, which meets the needs of real time and adaptability for autonomous underwater vehicle dynamic obstacle avoidance.

Obstacle avoidance path planning of unmanned submarine vehicle in ocean current environment based on improved firework-ant colony algorithm

In order to solve the unmanned underwater vehicle two-dimensional autonomous path planning problem in the environment affected by ocean current and obstacles, this paper applies the improved Fireworks-Ant Colony Hybrid Algorithm to solve it. Firstly, a two-dimensional Lamb vortex ocean current environment model with randomly distributed obstacles is established, and the circular obstacle is equivalent to a square grid. Then, the mathematical model of path planning is established considering the energy consumption cost, navigation time cost and navigation distance cost. Finally, the improved fireworks-ant colony hybrid algorithm is applied to solve the nonlinear optimization problem, and this algorithm is compared with the basic ant colony algorithm for simulation experiments in the four different marine environments. The experimental results show that this algorithm can quickly find the global optimal solution, and the more complex the environment, the more obvious its advantages. The algorithm proposed in this paper provides a new way for autonomous path planning of underwater vehicles.

A method of Space Characterization for underwater vehicle path planning

multi unmanned underwater vehicle (AUV) cooperative mission is the trend of underwater combat application. It is an important task to search for the target in unknown environment. This paper describes the design and development of planning spatial representation by vector description and mesh model combination. The method adopt the grid planning regional elements and grid attribute mark to construct the route search algorithm which can reduce the search time and space overhead and improve the planning efficiency in path planning process.

The Application of a Complex Composite Fractal Interpolation Algorithm in the Seabed Terrain Simulation

Seabed terrain modelling is one of the key technologies in the Subsea Environmental Information System, and this system is critical for underwater vehicle path planning. A composite fractal interpolation algorithm based on improved fractional Brownian motion (FBM) and an improved iterative function system (IFS) is proposed in this paper to increase the precision of the seabed terrain model for submarine topography and to account for the complexity and irregularity of fractal properties in each region. The MATLAB simulation experiment showed that fractal properties of the model built by the complex composite fractal interpolation algorithm were closer to real surface features. After calculation analysis, the model built by the complex composite fractal interpolation algorithm, when compared with the model built by the traditional interpolation algorithm or by the single fractal interpolation algorithm, had higher precision and was more suitable for path planning for underwater vehicles.

Integration of risk in hierarchical path planning of underwater vehicles

Abstract: This paper focuses on path planning problems integrating the risk of collision. This challenge is crucial regarding the use of autonomous underwater vehicles (AUVs) for inspection, maintenance and repair operations. The solving of this problem in a reasonable amount of time enables integration of path planning in the AUV control architecture and, by this way, enhances its autonomy capability. Classical approaches rely on the A-Star algorithm to solve this problem, but the heuristic associated to the collision risk appears to be inefficient in some cases. Based on the hierarchical technique, i.e., HPA-Star, another approach is proposed. It leads to paths close to the optimal ones calculated in a faster way. The performances are illustrated in the context of a multi criteria optimization: minimal length and minimal risk path planning.

3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles

In this paper, optimal paths in environments with obstacles for underwater vehicles are computed using a numerical solution of the nonlinear optimal control problem (NOCP). The underwater vehicle is modeled with six-dimensional nonlinear and coupled equations of motion, controlled by DC motors in all degrees of freedom. An energy performance index combined with a time consumption index is used. Both fixed and free final times are considered. Solving NOCP leads to a two point boundary value problem (TPBVP). Five intelligent evolutionary algorithms (EAs), which include genetic algorithm, memetic algorithm, particle swarm optimization, ant colony optimization and shuffled frog leaping algorithm, are applied to solve the NOCP. For comparison, a conjugate gradient penalty method is also used to solve the TPBVP. The simulation results show that the trajectories obtained by the intelligent methods are better than those of conjugate gradient method. After analyzing a simple path planning problem, the time-energy-optimal path planning problem in energetic environments is propounded and solved by EAs. The problem of static obstacle collision avoidance in an energetic environment is also studied.

Application of improved particle swarm optimization in path planning of underwater vehicles

The characteristics of the underwater vehicle's path planning in the ocean environment are as follows:broad planning range,relatively sparse obstacles,and inevitable impact of the ocean currents.The Particle Swarm Optimization(PSO)algorithm was adopted to realize the path planning of the underwater vehicle in the complex ocean environment.According to the parameters control strategies and topology models,an improved PSO algorithm with better constringency precision was obtained.During the programming,a fitness function was designed,which combined path length,ocean currents and shift cost of the underwater vehicle.By using this function,the adverse effects on the underwater vehicle's energy consumption and control performance caused by the ocean currents can be greatly reduced.The simulation results verify the effectiveness of this algorithm.Besides,the proposed algorithm can well meet the requirements of the path planning for the underwater vehicle in complex environment.

Two novel approaches for unmanned underwater vehicle path planning: constrained optimisation and semi-infinite constrained optimisation

In this paper, two novel approaches to unmanned underwater vehicle path planning are presented. The main idea of the first approach, referred to as Constrained Optimisation (CO) is to represent the free space of the workspace as a set of inequality constraints using vehicle configuration variables. The second approach converts robot path planning into a Semi-infinite Constrained Optimisation (SCO) problem. The function interpolation technique is adopted to satisfy the start and goal configuration requirements. Mathematical foundations for Constructive Solid Geometry (CSG), Boolean operations and approximation techniques are also presented to reduce the number of constraints, and to avoid local minima. The advantages of these approaches are that the mature techniques developed in optimisation theory which guarantee convergence, efficiency and numerical robustness can be directly applied to the robot path planning problem. Simulation results have been presented.