The Path Planning of AUV Based on D-S Information Fusion Map Building and Bio-Inspired Neural Network in Unknown Dynamic Environment

Zhu, Daqi; Li, Weichong; Yan, Ming; Yang, Simon X.

doi:10.5772/56346

Cited by 29 publications

(25 citation statements)

References 21 publications

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“…if follow-up is not collision-free then (17) Replan follow-up with Algorithm 2 (18) else (19) ← pass-by + follow-up (20) end if (21) else (22) break ( and [ , → ]. Since pass-by is obtained in the intermediate configuration determination process, follow-up is generated after pass-by is determined.…”

Section: Intermediate Configurationmentioning

confidence: 99%

“…Among them a biologically inspired neural network approach is presented in [17] to plan path for the autonomous underwater vehicle in unknown two-dimension (2D) environment, which is achieved via updating the environment maps according to Dempster-Shafer theory in steps. In [18] the online path planning problem with prescribed target in environment with unknown obstacles is considered and the neural networks trained by the reinforcement learning approach are adopted to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

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Reactive Path Planning Approach for Docking Robots in Unknown Environment

Cui

Yan

Wang

2017

Journal of Advanced Transportation

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Autonomous robots need to be recharged and exchange information with the host through docking in the long-distance tasks. Therefore, feasible path is required in the docking process to guide the robot and adjust its pose. However, when there are unknown obstacles in the work area, it becomes difficult to determine the feasible path for docking. This paper presents a reactive path planning approach named Dubins-APF (DAPF) to solve the path planning problem for docking in unknown environment with obstacles. In this proposed approach the Dubins curves are combined with the designed obstacle avoidance potential field to plan the feasible path. Firstly, an initial path is planned and followed according to the configurations of the robot and the docking station. Then when the followed path is evaluated to be infeasible, the intermediate configuration is calculated as well as the replanned path based on the obstacle avoidance potential field. The robot will be navigated to the docking station with proper pose eventually via the DAPF approach. The proposed DAPF approach is efficient and does not require the prior knowledge about the environment. Simulation results are given to validate the effectiveness and feasibility of the proposed approach.

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Section: Intermediate Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reactive Path Planning Approach for Docking Robots in Unknown Environment

Cui

Yan

Wang

2017

Journal of Advanced Transportation

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“…Evolutionary algorithms such as Particle Swarm Optimization (PSO) [17][18][19], Ant Colony Optimization (ACO) [20] and Genetic Algorithm (GA) [21] are suitable for multi-objective problems. Many other evolutionary algorithms such as Artificial Bee Colony (ABC) [22], Bacterial Foraging Optimization (BFO) [23], Bio Inspired Neural Networks [24,25], and Fire Fly algorithm [26] are often trapped in local optimum, and bear high computational cost. Moreover, they are highly sensitive to search space size and data representation scheme of problem [27,28].…”

Section: Introductionmentioning

confidence: 99%

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

Noreen¹,

Khan²,

Habib³

2016

ijacsa

205

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Abstract-Optimal path planning refers to find the collision free, shortest, and smooth route between start and goal positions. This task is essential in many robotic applications such as autonomous car, surveillance operations, agricultural robots, planetary and space exploration missions. Rapidly-exploring Random Tree Star (RRT*) is a renowned sampling based planning approach. It has gained immense popularity due to its support for high dimensional complex problems. A significant body of research has addressed the problem of optimal path planning for mobile robots using RRT* based approaches. However, no updated survey on RRT* based approaches is available. Considering the rapid pace of development in this field, this paper presents a comprehensive review of RRT* based path planning approaches. Current issues relevant to noticeable advancements in the field are investigated and whole discussion is concluded with challenges and future research directions.

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“…However, pure inertial navigation cannot satisfy the demands of long range and long endurance, due to the navigation error, which accumulates with time. Thus, a typical navigation scheme for AUVs is based on a high quality SINS combined with Doppler velocity log (DVL) [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

Dong

Sun

et al. 2017

Symmetry

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Abstract:As a typical navigation system, the strapdown inertial navigation system (SINS) is crucial for autonomous underwater vehicles (AUVs) since the SINS accuracy determines the performance of AUVs. Initial alignment is one of the key technologies in SINS, and initial alignment time and initial alignment accuracy affect the performance of SINS directly. As actual systems are nonlinear, the nonlinear filter is widely used to improve the accuracy of the initial alignment. Due to its higher precision and lower computational load, the cubature Kalman filter (CKF) has done well in state estimation. However, the noise characteristics need to be known exactly as prior knowledge, which is difficult or even impossible to achieve. Thus, the adaptive filter should be introduced in the initial alignment algorithm to suppress the uncertainty effect caused by the unknown system noise. Therefore, taking the nonlinearity and uncertainty into account, a novel initial alignment algorithm for AUVs is proposed in this manuscript, based on CKF and the adaptive variance components estimation (VCE) filter (VCKF). Additionally, the simulation and experiment results show that not only the accuracy, but also the convergence speed can be improved with this proposed method. The validity and superiority of this novel adaptive initial alignment algorithm based on VCKF are verified.

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The Path Planning of AUV Based on D-S Information Fusion Map Building and Bio-Inspired Neural Network in Unknown Dynamic Environment

Cited by 29 publications

References 21 publications

Reactive Path Planning Approach for Docking Robots in Unknown Environment

Reactive Path Planning Approach for Docking Robots in Unknown Environment

Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions

An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

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