Route planning algorithm for autonomous underwater vehicles based on the hybrid of particle swarm optimization algorithm and radial basis function

Zhou, Weidong; Xing, Zejing; Bai, Wei; Deng, Chao; Xie, Yaen; Wu, Xiande

doi:10.1177/0142331218785708

Cited by 17 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The commonly used local path planning algorithm 5 is divided into traditional algorithms and intelligent algorithms, including artificial potential field method, fuzzy logic, raster algorithm, neural network algorithm, and so on. In order to solve the path planning problem of basketball robots in dynamic environments, Lyu and Yin 6 proposed a relatively dynamic artificial potential field algorithm, which regards time as a one-dimensional parameter of the planning model, and moving obstacles in the extended model.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Zhi

Jiang

2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Aiming at the strong dependence on environmental information in traditional algorithms, the path planning of basketball robots in an unknown environment, and improving the safety of autonomous navigation, this article proposes a path planning algorithm based on behavior-based module control. In this article, fuzzy control theory is applied to the behavior control structure, and these two path planning algorithms are combined to solve the path planning problem of basketball robots in an unknown environment. First, the data of each sensor of the basketball robot configuration are simply fused. Then, the obstacle distance parameters in the three directions of front, left, and right are simplified and fuzzified. Then combined with the target direction parameters, the speed, and steering of the basketball robot are controlled by fuzzy rule reasoning to realize path planning. The simulation results show that the basketball robot can overcome the uncertainty in the environment, effectively achieve good path planning, verify the feasibility of the fuzzy control algorithm, and demonstrate the validity and correctness of the path planning strategy.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Zhi

Jiang

2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Additionally, refs. [14][15][16] features a study on using wavelet transform and support vector machine (SVM) algorithms to predict wind power. The above method is usually applied in the scenario of long-term power dispatching, where the methods are tested in a time span from 30 min to a few hours.…”

Section: Introductionmentioning

confidence: 99%

Ultrashort-Term Power Fluctuation Forecasting Based on the Prediction of the Shipborne Panel Tilt Angle

et al. 2020

View full text Add to dashboard Cite

When a solar ship is navigating in the ocean, the swaying motion of a photovoltaic panel will affect the output power of the photovoltaic (PV) power generation system more frequently and violently. In addition to considering multiple climatic factors, this paper also adopts a ship swaying motion and radiation level of sunlight to establish a suitable calculation model for the output power of photovoltaic systems, which are rarely considered at the same time in previous studies, and also to make ultrashort-term power predictions. Furthermore, this paper proposes a multilayer heterogeneous particle swarm optimization (PSO) algorithm to design the weights and thresholds of a long short-term memory (LSTM) neural network to improve the accuracy of forecasting the changes of a photovoltaic panel’s angle, which is used for accurate power output prediction for the purpose of power planning. The case analysis shows the effectiveness of the algorithm, which provides a more reliable method for designing a power prediction system.

show abstract

“…In order to deal with the path planning problem for AUV, many path planning approaches have been proposed in recent years, which can be categorized into graph search-based approaches [10]- [14], tree search-based approaches [16], [17], artificial potential field (APF)-based approaches [18]- [23], and evolutionary algorithms (EAs)based approaches [24]- [28]. In the graph search-based approaches, Wang et al [10] propose a hybrid A * algorithm based path planner to handle path planning problems for AUV in static 2-D environment.…”

Section: Introductionmentioning

confidence: 99%

“…For the dynamic factors, they take ocean flows and moving obstacles into account. Zhou et al [28] propose a hybrid PSO algorithm to handle path planning problems for AUV in dynamic 3-D environment. Also, they have taken ocean flows into consideration.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Path Planning for AUVs in Dynamic Environments: An EDA-Based Learning Fixed Height Histogram Approach

et al. 2019

View full text Add to dashboard Cite

Autonomous underwater vehicles (AUVs) are robots that require path planning to complete missions in different kinds of underwater environments. The goal of path planning is to find a feasible path from the start-point to the target-point in a given environment. In most practical applications, environments have dynamic factors, such as ocean flows and moving obstacles, which make the AUV path planning more challenging. This paper proposes an estimation of distribution algorithm (EDA) based approach, termed as learning fixed-height histogram (LFHH) to solve path planning problems for AUVs in dynamic environment. The LFHH uses a learning transformation strategy (LTS) to improve its accuracy and convergence speed. Besides, a smooth method is employed to accelerate the speed of finding feasible paths. Moreover, a planning window is adopted to help handle dynamic factors. LFHH is tested in both complex 2-D and 3-D environments with time-variant dynamic factors, and experimental results validate the effectiveness of LFHH.INDEX TERMS Autonomous underwater vehicles (AUVs), learning fixed height histogram (LFHH), estimation of distribution algorithm (EDA), dynamic environments, path planning.

show abstract

Route planning algorithm for autonomous underwater vehicles based on the hybrid of particle swarm optimization algorithm and radial basis function

Cited by 17 publications

References 18 publications

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Ultrashort-Term Power Fluctuation Forecasting Based on the Prediction of the Shipborne Panel Tilt Angle

Intelligent Path Planning for AUVs in Dynamic Environments: An EDA-Based Learning Fixed Height Histogram Approach

Contact Info

Product

Resources

About