Two‐dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm

Yan, Zheping; Zhang, Jinzhong; Yang, Zewen; Tang, Jialing

doi:10.1002/cpe.6140

Cited by 20 publications

(10 citation statements)

References 39 publications

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“…The results confirm that the proposed technique outperforms the established techniques. Z et al [140] applied a WOA based upon a forward-looking sonar to realize two-dimensional optimal path planning for an AUV. In [141], a failure diagnosis method based on variational mode decomposition and optimized via an IWOA was proposed, and the effectiveness of the method was verified via a failure experiment on a test stand.…”

Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Swarm algorithms and evolutionary algorithms are widely adopted for solving the optimal path planning algorithms with multiple constraints [ 40 , 41 , 42 , 43 , 44 ]. Whale optimization (WO) based path planning for an underwater robot is mentioned in [ 45 ], where the optimization techniques are used for generating a path with safe and minimal fuel consumption. An improved sparrow search algorithm for path-planning for a mobile robot is reported in [ 46 ].…”

Section: Related Workmentioning

confidence: 99%

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Pathmakumar

Muthugala

Samarakoon

et al. 2022

Sensors

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Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

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“…The improved ACO algorithm uses improved pheromone update rule and heuristic function based on PSO algorithm to make AUV find the optimal path by connecting the chosen nodes of the undersea environment while avoiding the collision with the complex undersea terrain. Besides, optimization algorithms such as whale optimization algorithm (WOA) [46] and invasive weed optimization (IWO) [47] are also used in optimal path planning.…”

Section: Introductionmentioning

confidence: 99%

Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

Zhao

Zhang

Liu

et al. 2023

Asian Journal of Control

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In the real‐world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven terrain because of the constraints of obstacle avoidance and path length is a challenge for tracked robots. In this paper, a stability‐based path planning framework for tracked robot is proposed to reduce the risk of rollover when the tracked robot passes through uneven terrain. First, a virtual plane method is proposed to estimate the attitude of tracked robot. Second, on this basis, a dynamic high‐stability path evaluation algorithm for tracked robot based on force angle stability margin (FASM) is proposed, which transforms the stability‐based path planning problem into a hypergraph problem. Moreover, considering that the optimization problem is strongly nonlinear and nonconvex, a hybrid algorithm of covariance matrix adaptive evolution strategy (CMAES) and Levenberg–Marquardt (LM) is designed under the framework of generalized graph optimization (G2O) to improve the solution efficiency. Finally, simulation and experiments show that the stability‐based path planning framework can effectively plan the high‐quality path, and the maximum stability of the tracked robot is 0.9156 when the robot crosses uneven terrain using optimal path 2.

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Two‐dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm

Cited by 20 publications

References 39 publications

A survey on several new popular swarm intelligence optimization algorithms

A survey on several new popular swarm intelligence optimization algorithms

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

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