Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle

Ataei, Mansour; Yousefi‐Koma, Aghil

doi:10.1016/j.robot.2014.10.007

Cited by 109 publications

(53 citation statements)

References 17 publications

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“…Evolutionary algorithms are population based optimization methods that can be implemented on a parallel machine with multiple processors to speed up computation (Roberge et al 2013). Relatively, they are efficient methods for dealing with path planning as a Non-deterministic Polynomial-time (NP) hard problem (M. Zadeh et al 2016-c;Ataei and Yousefi-Koma 2015), and fast enough to satisfy the time restrictions of real-time applications. The Particle Swarm Optimization (Zheng et al 2005) and Genetic Algorithm (Nikolos et al2003;Zheng et al 2005;Kumar and Kumar 2010) are two popular types of optimization algorithms applied successfully in path planning application.…”

Section: Path/trajectory Planning Approachesmentioning

confidence: 99%

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

Zadeh¹,

Powers²,

Sammut³

et al. 2016

Soft Comput

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Expansion of today's underwater scenarios and missions necessitates the requestion for robust decision making of the Autonomous Underwater Vehicle (AUV); hence, design an efficient decision making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a Global Route Planner (GRP) and a Local Path Planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively guided to the target of interest in the context of an uncertain underwater environment. The architecture involves three main modules: The GRP module at the top level deals with the task priority assignment, mission time management, and determination of a feasible route between start and destination point in a large scale environment. The LPP module at the lower level deals with safety considerations and generates collision-free optimal trajectory between each specific pair of waypoints listed in obtained global route. Re-planning module tends to promote robustness and reactive ability of the AUV with respect to the environmental changes. The experimental results for different simulated missions, demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles autonomy in terms of mission productivity, mission time management, and vehicle safety.

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Section: Path/trajectory Planning Approachesmentioning

confidence: 99%

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

Zadeh¹,

Powers²,

Sammut³

et al. 2016

Soft Comput

View full text Add to dashboard Cite

show abstract

“…The dynamics that have been used are mainly based on the analysis presented by various researchers [3]. The generalizedequations (2.1) and (2.2)of motion for AUV, relative to an inertial frame, can be given by six coupled non-linear differential equations: …”

Section: Dynamics Of the Underwatermentioning

confidence: 99%

“…Due to the difficulty in interaction between the nonlinear dynamic model of AUV and the environment, motion planning for AUV in an ambiguous and cluttered environment leads to demanding control problem [3].Modern control may provide superior execution by adapting the ambiguities of hydrodynamics as well as revealing resistance to in stabilities. Foremost intent is to organize an order of appropriate paths based on updated parameters through online learning method, which enables the vehicle to fulfill its mission by showing obstacle avoidance and target seeking behavior in a combined manner [4].…”

Section: Introductionmentioning

confidence: 99%

Navigational Analysis for Under water Mobile Robot based on Multiple ANFIS Approach

Kundu¹,

Parhi²

2015

JAMES

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Multiple neuro-fuzzy inference systems using hybrid learning algorithm as an adaptation mechanism have been focused here for navigation of autonomous underwater vehicle (AUV).The underwatervehiclecanbeexhibitedassix-dimensionalnonlinearandcoupled equations of motion associated with variations of hydro dynamic coefficients which are difficult to model in a realistic manner. Without earlier acquaintance, the feed-forward neuro-fuzzy controller can be directed to obtain the unknown parameters of the model which may aid motion planning strategy of underwaterrobotbyoverlookingthenonlineareffectsoftheAUVdynamics.By amending fuzzy membership function of neural networks, the benefits of fuzzy logic and neural network can be mingled, such as capability of FIS to deal with uncertainty, employing human perception and comprehensive approximation as well as adapting competence of neural networks. ANFIS has been trained with the hybrid-learning mechanism which employs back-propagation-based gradient descent approach and least squares estimate (LSE)to estimate parameters of the model. This approach instigates faster decision-making, obstacle avoidance and also tracking targets. The simulated analysis may authenticate that the heuristic navigational approach is able to negotiate with chaotic environment during navigation of under-water robot.

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“…There are various examples of evolution based applications of path planning and routing-scheduling approaches. A Non-Dominated Sorting Genetic Algorithm (NSGA-II) is employed for AUV's waypoint guidance and offline path planning [10]. M.Zadeh et al, designed an online Differential Evolution (DE) based path planner for a single AUV's operation in a dynamic ocean environment [11].…”

Section: Introductionmentioning

confidence: 99%

Efficient Deployment and Mission Timing of Autonomous Underwater Vehicles in Large-Scale Operations

MahmoudZadeh

2019

Advanced Data Mining and Applications

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This study introduces a connective model of routing-local path planning for Autonomous Underwater Vehicle (AUV) time efficient maneuver in long-range operations. Assuming the vehicle operating in a turbulent underwater environment, the local path planner produces the water-current resilient shortest paths along the existent nodes in the global route. A re-routing procedure is defined to re-organize the order of nodes in a route and compensate any lost time during the mission. The Firefly Optimization Algorithm (FOA) is conducted by both of the planners to validate the model's performance in mission timing and its robustness against water current variations. Considering the limitation over the battery life time, the model offers an accurate mission timing and real-time performance. The routing system and the local path planner operate cooperatively, and this is another reason for model's real-time performance. The simulation results confirms the model's capability in fulfilment of the expected criterion and proves its significant robustness against underwater uncertainties and variations of the mission conditions.

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Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle

Cited by 109 publications

References 17 publications

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

Navigational Analysis for Under water Mobile Robot based on Multiple ANFIS Approach

Efficient Deployment and Mission Timing of Autonomous Underwater Vehicles in Large-Scale Operations

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