Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms

MahmoudZadeh, Somaiyeh; Yazdani, Amirmehdi; Sammut, Karl; Powers, David M. W.

doi:10.1016/j.asoc.2017.10.025

Cited by 85 publications

(34 citation statements)

References 34 publications

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“…Evolutionary algorithms, such as the Genetic algorithm (GA), are usually studied under network environments [66][67][68][69] that are related to CFPP. Additionally, several algorithms are developed for under coordinate system conditions [70][71][72][73]. Some novel algorithms that are based on GA are provided from related works [74][75][76][77][78].…”

Section: Heuristic Based Algorithmmentioning

confidence: 99%

A Performance Review of Collision-Free Path Planning Algorithms

Shin

Chae

2020

Electronics

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Path planning for mobile agents is one of the areas that has drawn the attention of researchers’, as evidenced in the large number of papers related to the collision-free path planning (CFPP) algorithm. The purpose of this paper is to review the findings of those CFPP papers and the methodologies used to generate possible solutions for CFPP for mobile agents. This survey shows that the previous CFPP papers can be divided based on four characteristics. The performance of each method primarily used to solve CFPP in previous research is evaluated and compared. Several methods are implemented and tested in same computing environment to compare the performance of generating solution in specified spatial environment with different obstacles or size. The strengths and weakness of each methodology for CFPP are shown through this survey. Ideally, this paper will provide reference for new future research.

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Section: Heuristic Based Algorithmmentioning

confidence: 99%

A Performance Review of Collision-Free Path Planning Algorithms

Shin

Chae

2020

Electronics

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“…The fireflies' brightness decreases by distance and the brighter fireflies attract the less bright ones; hence, their attraction is proportional to their brightness and their relative distance. Attraction of a firefly i toward the brighter firefly j is calculated as follows: (9) the ij is the distance between fireflies i and j; β0 is the attraction factor at =0, α0 and αt are the initial randomness scaling value and the randomization parameter, respectively. αt tunes the randomness of fireflies' movement in each iteration.…”

Section: Foa On Mission Routing and Path Planning Approachmentioning

confidence: 99%

“…Hence, deterministic and heuristic algorithms cannot be appropriate for real-time applications as these methods are computationally expensive in large spaces [8]. Meta-heuristics are another alternative group of algorithms for solving complex problems that offer near optimal solutions in a very quick computation [9] and is appropriate for the purpose of this study.…”

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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“…Despite meta-heuristics do not necessarily produce pure optimal solutions, but they are computationally fast and efficient and especially appropriate for the real-time applications [18,19]. The Particle Swarm Optimization (PSO) [18,20] and Quantum-based PSO (QPSO) [21] are two swarm-based optimization methods applied successfully on AUV path planning problem. An offline three-dimensional path planner based on a non-dominated sorting genetic algorithm (NSGA-II) is proposed for waypoint guidance of an AUV [22].…”

Section: Meta-heuristic Optimization Algorithm: the State Of The Art 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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Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms

Cited by 85 publications

References 34 publications

A Performance Review of Collision-Free Path Planning Algorithms

A Performance Review of Collision-Free Path Planning Algorithms

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

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

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