Three-Dimensional Underwater Path Planning Based on Modified Wolf Pack Algorithm

Zhang, Lanyong; Zhang, Lei; Liu, Sheng; Zhang, Jiajia; Papavassiliou, Christos

doi:10.1109/access.2017.2765504

Cited by 42 publications

(21 citation statements)

References 17 publications

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“…Aghababa [25] formalized the 3-D path planning with static obstacles as a nonlinear optimal control problem and applied five EAs to solve the problem. Zhang et al [26] improved the wolf pack algorithm to solve the 3-D underwater path planning considering terrain obstacles in the peak shape. Yan et al [27] classified irregular obstacles into four types and accordingly designed obstacle avoidance rules.…”

Section: B Auv Path Planning Under Dense Obstaclesmentioning

confidence: 99%

“…However, despite the rich achievements as reviewed above, some deficiencies still exist. On the one hand, some works merely consider 2-D environments [23], [24]; on the other hand, many algorithms are designed on the hypothesis that the obstacles are of a small number and in a certain regular shape [26], but the hypothesis actually not always holds in reality. Even among the few works designed for irregular obstacles, most of them such as [27] lack a versatility due to the use of some sophisticated classification strategies.…”

Section: B Auv Path Planning Under Dense Obstaclesmentioning

confidence: 99%

“…To optimize more than one cost as aforementioned, there are two popular approaches: 1) optimize the multiple costs simultaneously using specialized multiobjective algorithms and 2) integrate the costs into a single optimization objective function using techniques, such as a weighted sum of the objectives [43], and then optimize it by singleobjective algorithms. In the field of path planning, weightbased single optimization is quite popular, and easy to use [16], [26], [44]- [46]. For the setting of cost weights, previous works generally believe that the weights can be reasonably set according to some professional knowledge, e.g., the specific task type or priority [45] or the user preference [46].…”

Section: B Path Optimization Objectivementioning

confidence: 99%

See 2 more Smart Citations

ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles

Xue

Chen

et al. 2019

IEEE Trans. Evol. Computat.

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Path planning is one of the most important problems in the development of autonomous underwater vehicles (AUVs). In some common AUV missions, e.g., wreckage search for rescue, an AUV is often required to traverse multiple targets in a complex environment with dense obstacles. In such case, the AUV path planning problem becomes even more challenging. In order to address the problem, this paper develops a two-layer algorithm, namely ACO-A*, by combining the ant colony optimization (ACO) with the A* search. Once a mission with a set of arbitrary targets is assigned, ACO is responsible to determine the traveling order of targets. But, prior to ACO, a cost graph indicating the necessary traveling costs among targets must be quickly established to facilitate traveling order evaluation. For this purpose, a coarse-grained modeling with a representativebased estimation (RBE) strategy is proposed. Following the order obtained by ACO, targets will be traversed one by one and the pairwise path planning to reach each target can be performed during vehicle driving. To deal with the dense obstacles, A* is adopted to plan paths based on a fine-grained modeling and an admissible heuristic function is designed for A* to guarantee its optimality. Experiments on both synthetic and realistic scenarios have been designed to validate the efficiency of the proposed ACO-A*, as well as the effectiveness of RBE and the necessity of A*.

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Section: B Auv Path Planning Under Dense Obstaclesmentioning

confidence: 99%

Section: B Auv Path Planning Under Dense Obstaclesmentioning

confidence: 99%

Section: B Path Optimization Objectivementioning

confidence: 99%

See 1 more Smart Citation

ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles

Xue

Chen

et al. 2019

IEEE Trans. Evol. Computat.

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“…Wolf pack algorithm (WPA) [19] is a relatively new and promising member of swarm intelligence-based algorithms that model the cooperative hunting behavior of wolf pack. It has been proved an efficient optimizer for solving many nonlinear and complex optimization problems by successful applications in image processing [20], power system control [21], robot path planning [22], and static MKPs [23]. Many derivative versions of WPA also have been designed for solving different problems, such as binary WPA (BWPA) for 0-1 ordinary knapsack problem [24], improved binary WPA (IBWPA) for MKPs [23], and discrete WPA (DWPA) for TSP [25].…”

Section: Introductionmentioning

confidence: 99%

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Xiao

2020

Research

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Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial. Moreover, many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment. In this study, based on binary wolf pack algorithm (BWPA), combining with flexible population updating strategy, a flexible binary wolf pack algorithm (FWPA) is proposed. Then, FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems, which have numerous practical applications. To the best of our knowledge, this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem. By comparing two state-of-the-art algorithms with the basic BWPA, the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.

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“…An underwater acoustic sensor network with one mobile surface node to collect data from multiple underwater nodes was investigated in [ 12 ], whereby the mobile destination requests retransmission from each underwater node individually by employing the traditional automatic-repeat-request (ARQ) protocol. Zhang et al [ 13 ] aimed to overcome the shortcomings of the wolf-pack algorithm (WPA) and improve three intelligent behaviors of the WPA, namely, scouting, summoning, and beleaguering. As a survey paper, Zeng et al [ 14 ] studied path planning for the persistent autonomy of autonomous underwater vehicles.…”

Section: Introductionmentioning

confidence: 99%

Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks

Cai

Zhang

2018

Sensors

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Sensory data collection is one of the most important concerns in underwater sensor networks (USNs). Because full connectivity cannot be guaranteed, mobile data gathering with autonomous underwater vehicles (AUVs) is widely used in sparse three-dimensional (3D) USNs to solve energy-imbalance problems between different sensor nodes. AUVs with relatively abundant energy and storage can collect sensory data from one sensor node to transmit to another node, so as to avoid energy-intensive multi-hop transmission. As a result, movement control strategy and data collecting path planning for AUVs are very crucial for the performance of data acquisition. This paper proposes a smooth 3D Dubins curves based mobile data gathering mechanism to overcome the kinematic nonholonomic constraints of AUVs. The objective of our proposed method is to collect sensory data along smooth 3D Dubins paths, which are interpolated by continuous Bezier curves in the Z-axis from 2D Dubins curves. Extensive simulation results verify that the proposed method has a more efficient performance in terms of path smoothness and energy consumption; thus it is very suitable for mobile data collection in 3D underwater sensor networks.

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Three-Dimensional Underwater Path Planning Based on Modified Wolf Pack Algorithm

Cited by 42 publications

References 17 publications

ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles

ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks

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Three-Dimensional Underwater Path Planning Based on Modified Wolf Pack Algorithm

Cited by 42 publications

References 17 publications

ACO-A*: Ant Colony Optimization Plus A* for 3-D Traveling in Environments With Dense Obstacles

ACO-A*: Ant Colony Optimization Plus A* for 3-D Traveling in Environments With Dense Obstacles

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks

Contact Info

Product

Resources

About

ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles

ACO-A: Ant Colony Optimization Plus A for 3-D Traveling in Environments With Dense Obstacles