Shell space decomposition based path planning for AUVs operating in a variable environment

Zeng, Zheng; Lammas, Andrew; Sammut, Karl; He, Fangpo; Tang, Youhong

doi:10.1016/j.oceaneng.2014.09.001

Cited by 73 publications

(41 citation statements)

References 25 publications

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“…To validate the robustness of the proposed method, its performance is examined under uncertain operating conditions. A set of 100 Monte Carlo runs is conducted to statistically analyse the performance of the planning algorithm [17]. For this purpose, pseudorandom initial and final conditions equivalent to different initial and final poses of the AUV and the docking station in the problem space together with random current vectors are generated using both Gaussian and uniform distributions.…”

Section: Simulation Resultsmentioning

confidence: 99%

Real-time quasi-optimal trajectory planning for autonomous underwater docking

Yazdani

Sammut

Lammas

et al. 2015

2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)

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-In this paper, a real-time quasi-optimal trajectory planning scheme is employed to guide an autonomous underwater vehicle (AUV) safely into a funnel-shape stationary docking station. By taking advantage of the direct method of calculus of variation and inverse dynamics optimization, the proposed trajectory planner provides a computationally efficient framework for autonomous underwater docking in a 3D cluttered undersea environment. Vehicular constraints, such as constraints on AUV states and actuators; boundary conditions, including initial and final vehicle poses; and environmental constraints, for instance no-fly zones and current disturbances, are all modelled and considered in the problem formulation. The performance of the proposed planner algorithm is analyzed through simulation studies. To show the reliability and robustness of the method in dealing with uncertainty, Monte Carlo runs and statistical analysis are carried out. The results of the simulations indicate that the proposed planner is well suited for real-time implementation in dynamic and uncertain environment.

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Section: Simulation Resultsmentioning

confidence: 99%

Real-time quasi-optimal trajectory planning for autonomous underwater docking

Yazdani

Sammut

Lammas

et al. 2015

2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)

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“…The water current map can be captured from remote observations provided by satellite or from numerical estimation models. Different type of predictive ocean current models have been constructed previously [21,[29][30][31]. In this research, information of static 2D turbulent current has been employed, as the deep ocean current fields do not change immediately.…”

Section: Mathematical Representation Of the Waypoint Cluttered Ocean mentioning

confidence: 99%

“…This study is a part of the "Autonomous Underwater Mission and Exploration Project" conducting at the Centre for Maritime Engineering, Control and Imaging, Flinders University, Australia. As the performance validation of the DEFO model has been completed successfully in this paper, as a future work, the DEFO model will be implemented on-board the under developing Flinders AUVs [11,21] for experimental evaluation and further investigations.…”

mentioning

confidence: 99%

Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment

MahmoudZadeh

Powers

Sammut

et al. 2018

J Intell Robot Syst

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This paper presents a hybrid route-path planning model for an Autonomous Underwater Vehicle's task assignment and management while the AUV is operating through the variable littoral waters. Several prioritized tasks distributed in a large scale terrain is defined first; then, considering the limitations over the mission time, vehicle's battery, uncertainty and variability of the underlying operating field, appropriate mission timing and energy management is undertaken. The proposed objective is fulfilled by incorporating a route-planner that is in charge of prioritizing the list of available tasks according to available battery and a pathplaner that acts in a smaller scale to provide vehicle's safe deployment against environmental sudden changes. The synchronous process of the task assign-route and path planning is simulated using a specific composition of Differential Evolution and Firefly Optimization (DEFO) Algorithms. The simulation results indicate that the proposed hybrid model offers efficient performance in terms of completion of maximum number of assigned tasks while perfectly expending the minimum energy, provided by using the favorable current flow, and controlling the associated mission time. The Monte-Carlo test is also performed for further analysis. The corresponding results show the significant robustness of the model against uncertainties of the operating field and variations of mission conditions. Fig.4. (a) Path adaption to current arrows in a static current map; (b) Path behavior in avoiding colliding uncertain forbidden zones and obstacles in presence of current flow; (c) Paths behavior in recognizing forbidden coastal areas and adapting current flows.

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“…3 as realized within the SITL simulation environment that has been developed at the Flinders University to test and evaluate different guidance strategies for the Flinders AUV. This simulation environment incorporates realistically modeled components of the vehicle and emulates a real vehicle behavior while operating in a maritime environment [27][28][29]. As shown in Fig.…”

Section: Proposed Control Architecture and Requirementsmentioning

confidence: 99%