AUV trajectory  optimization
213011041
Distributed implementation of nonlinear model predictive control for AUV trajectory tracking
This paper studies the trajectory tracking control of an autonomous underwater vehicle (AUV) We investigate the nonlinear model predictive control (NMPC) method looking for possible approaches to alleviate the heavy computational burden. Novel distributed NMPC algorithms are developed exploiting the dynamic properties of the AUV motion. By appropriately decomposing the original optimization problems into smaller size subproblems and then solving them in a distributed manner, the expected floating point operations (flops) can be reduced dramatically. We show that the convergence of the AUV trajectory can be guaranteed by the proposed contraction constraints in the decomposed subproblems. Recursive feasibility and closed loop stability are proved. Taking advantage of the guaranteed stability, a real time distributed implementation algorithm is further developed to automatically trade off between control performance and computational complexity. Extensive simulation studies on the Falcon AUV model demonstrate the effectiveness and robustness of the proposed approach.
Autom.
Chao Shen Yang Shi
53096104
Observability Degree Based AUV Single Beacon Navigation Trajectory Optimization with Range Only Measurements
Aiming at the problem of autonomous underwater vehicle navigation trajectory optimization using single beacon location under direct route condition, a nonlinear system model for AUV single beacon navigation is established, and the linearized system model with error states is constructed by polar coordinate transformation and simplification. Then, current disturbance is considered. To find out the optimum path to utilize range only measurements, a novel observability degree based analysis method is proposed, which gives a quantitative insight into convergence characteristics of the error states by using the eigenvalues of the normalized error covariance matrix. Simulation experiments are done to test convergence characteristics of AUV integrated navigation error states with single beacon range only measurements under direct route control conditions. The experimental results show that the proposed control method is effective, and it has an important engineering application value and provides us with an optimized path.
Complex.
Huapeng Yu Xu Zhou
213929611
Optimal robust trajectory tracking control of a X rudder AUV with velocity sensor failures and uncertainties
Abstract This paper presents an optimal robust control method for trajectory tracking of a X rudder autonomous underwater vehicle (AUV) subjects to velocity sensor failures and uncertainties. Two reduced order extended state observers (ESOs) are designed to estimate the surge and heave velocities, and the estimated values are used to replace all the linear velocity related parameters in controller design, which helps releasing the requirements of linear velocities measurement and makes the controller robust against linear velocity sensor failures. In kinematics control loop, line of sight (LOS) guidance law and Lyapunov based control are employed, and the unknown attack angle is calculated based on the estimated linear velocities. In dynamics control loop, a robust disturbance rejection control law is constructed using disturbance observers and modified terminal sliding mode control. Moreover, a multi objective optimization method is proposed to achieve X rudder allocation, which is not only energy efficient but also robust against rudder failures, and helps tackling the rudder input saturation problem at the same time. Finally, comparative numerical simulations are provided to demonstrate the robustness and effectiveness of the proposed approach.
Yingkai Xia Kan Xu Wenjin Wang Guohua Xu Xianbo Xiang Ye Li
174819017
Trajectory Optimization for Underwater Vehicles in Time Varying Ocean Flows
We consider the problem of generating optimal planar trajectories for marine vehicles taking into account the effect of time varying ocean currents. This is motivated by the need for economic trajectory generation in long duration operations, and it becomes even more important in scenarios where the magnitude of the ocean current speed is comparable to the maximum vehicle speed, as illustrated in our experiments. We propose a solution based on a simple motion model, dynamic programming and high resolution ocean models. Optimal trajectories are generated under the assumption that the vehicle is capable of reaching the destination even when the matching condition is violated in some segments of the path. Numerical results show that the method is appropriate for practical generation of feasible trajectories in spatially constrained areas with strong currents.
2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)
Miguel Aguiar Joao Borges de Sousa Joao Miguel Dias Jorge Estrela da Silva Renato Mendes Americo Ribeiro
42087210
Integrated Path Planning and Tracking Control of an AUV: A Unified Receding Horizon Optimization Approach
This paper attempts to develop a unified receding horizon optimization (RHO) scheme for the integrated path planning and tracking control of an autonomous underwater vehicle (AUV) Considering that the effective sensing range of onboard sensors is practically short, we formulate the path planning into RHO problems with the spline path template. The planned path is subsequently viewed as the state trajectory of a virtual reference system having the same kinematic and dynamic properties as the AUV's. Appropriately constructed error dynamics makes the AUV tracking control equivalent to the regulation problem of the error dynamic system, which facilitates the derivation of theoretical results via nonlinear MPC techniques. The model predictive control (MPC) tracking controller is designed so that closed loop stability can be ensured. Due to the inherent RHO nature, both the path planning and tracking control are incorporated into an unified scheme. Simulation studies are conducted using a realistic dynamic model of the Falcon AUV, which was created in our previous experimental work. The simulation results demonstrate the effectiveness of the proposed control algorithm.
IEEE/ASME Transactions on Mechatronics
Chao Shen Yang Shi Bradley J Buckham
205009322
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. A QPSO algorithm is introduced for AUV path planners.Important optimization techniques applied to AUV path planning are compared in several test scenarios.Monte Carlo trials were also run to analyse the performance of these optimization techniques.The weaknesses and strengths of each optimization technique have been stated.
Robotics Auton. Syst.
Zheng Zeng Karl Sammut Lian Lian Fangpo He Andrew Lammas Youhong Tang
218797411
An AUV Assisted Data Gathering Scheme Based on Clustering and Matrix Completion for Smart Ocean
The oceans cover more than 71% of the Earth's surface and have a surging amount of data. It is of great significance to seek energy effective and ultrareliable communication and transmission mechanism for effectively gathering abundant maritime data. In this article, we propose an autonomous underwater vehicle (AUV) assisted data gathering scheme based on clustering and matrix completion (ACMC) to improve the data gathering efficiency in the underwater wireless sensor network (UWSN) Specifically, we first improve the <inline formula> <tex math notation="LaTeX"$K$ /tex math>/inline formula> means algorithm by adopting the Elbow method to determine the optimal <inline formula> <tex math notation="LaTeX"$K$ /tex math>/inline formula> and setting a distance threshold to select the separate initial cluster centers. Then, we introduce a two phase AUV trajectory optimization mechanism to effectively reduce the trajectory length of the AUV. In the first phase, the optimized trajectory of the AUV is planned by adopting the greedy algorithm. In the second phase, the ordinary nodes close to the AUV trajectory are selected as secondary cluster heads to share the workload of cluster heads. Finally, we present an in cluster data collection mechanism based on matrix completion. An extensive experiment validates the effectiveness of our proposed scheme in terms of energy and data collection delay.
IEEE Internet of Things Journal
Mingfeng Huang Kuan Zhang Zhiwen Zeng Tian Wang Yuxin Liu
208094557
Path Planning in Multiple AUV Systems for Difficult Target Traveling Missions: A Hybrid Metaheuristic Approach
Multiple autonomous underwater vehicles (AUVs) are popular for challenging submarine missions. In this article, we focus on the multi AUV path planning for a common class of missions that need to traverse lots of mission targets in large and complex environments. Given that AUVs are often launched from a movable surface vehicle, e.g. a ship, a multi AUV target traveling problem is formulated with a requirement of surface point location. Thereafter, a hybrid metaheuristic approach is developed by sequentially performing a cube based environment modeling, cost map building, voyage planning, and detailed trajectory planning. Specifically, the shortest path faster algorithm (SPFA) is adopted to build a cost map among targets and candidate surface points, and the A* search is utilized for trajectory planning. The optimality of both SPFA and A* can indeed be guaranteed. Thus, voyage planning becomes critical and an algorithm namely DE C ACO is proposed by combining the ant colony optimization (ACO) and the differential evolution (DE) with a cluster based adjustment strategy, i.e. DE C. DE C and ACO evolve in parallel for surface point location and voyage generation, respectively. Experiments based on realistic bathymetries are conducted and the results validate the effectiveness and efficiency of the proposed DE C ACO.
IEEE Transactions on Cognitive and Developmental Systems
Xue Yu Wei-neng Chen Xiao-Min Hu Tianlong Gu Huaqiang Yuan Yuren Zhou Jinhan Zhang
58641873
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.
Sensors
Zheping Yan Jiyun Li Yi Wu Gengshi Zhang
61288210
TRAJECTORY OPTIMIZATION USING PSEUDOSPECTRAL METHODS FOR A MULTIPLE AUTONOMOUS UNDERWATER VEHICLE TARGET TRACKING PROBLEM
of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science TRAJECTORY OPTIMIZATION USING PSEUDOSPECTRAL METHODS FOR A MULTIPLE AUTONOMOUS UNDERWATER VEHICLE TARGET TRACKING PROBLEM By Adam J. Franklin May 2012 Chair: Anil V. Rao Cochair: Warren E. Dixon Major: Mechanical Engineering In this work, a method is presented for solving a multiple AUV target tracking problem using pseudospectral methods. The multiple AUV target tracking problem involves generating a trajectory that minimizes the sum of the distances from each AUV to the target over the entire trajectory. The trajectory generated for each vehicle must not violate the distance constraint between any other vehicle or the target. The multiple AUV target tracking problem is solved as an optimal control problem using the open source optimal control software GPOPS and a nonlinear programming problem solver SNOPT. The results of the multiple AUVs target tracking problem for two different target scenarios are analyzed and validated.
Adam J Franklin