A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

Guo, Na; Li, Caihong; Gao, Tengteng; Guo-ming, Liu; Li, Yongdi; Wang, Di

doi:10.1155/2021/5524232

Cited by 18 publications

(6 citation statements)

References 39 publications

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“…Chen et al [29] implemented an UAV obstacle avoidance model based on feed-forward neural network (FNN), in which the UAV can effectively avoid obstacles and plan a reasonable path using the trained FNN. However, the FNN only considers the current environmental input, and path planning as a complex decision problem is not accurate enough to make a single-step decision only by relying on the current environmental situation.…”

Section: Related Workmentioning

confidence: 99%

Real-Time UAV Path Planning Based on LSTM Network

Zhang,

Guo,

Zheng

et al. 2024

J. of Syst. Eng. Electron.

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To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle (UAV) real-time path planning problem, a real-time UAV path planning algorithm based on long shortterm memory (RPP-LSTM) network is proposed, which combines the memory characteristics of recurrent neural network (RNN) and the deep reinforcement learning algorithm. LSTM networks are used in this algorithm as Q-value networks for the deep Q network (DQN) algorithm, which makes the decision of the Q-value network has some memory. Thanks to LSTM network, the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment. Besides, the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning, so that the UAV can more reasonably perform path planning. Simulation verification shows that compared with the traditional feed-forward neural network (FNN) based UAV autonomous path planning algorithm, the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.

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Section: Related Workmentioning

confidence: 99%

Real-Time UAV Path Planning Based on LSTM Network

Zhang,

Guo,

Zheng

et al. 2024

J. of Syst. Eng. Electron.

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“…LSTM [16] is a long-term and short-term memory network developed from RNN (Circulating Neural Network) [17].…”

Section: A Lstmmentioning

confidence: 99%

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

2022

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In order to solve the "minimum trap" of artificial potential field method and the limitation of traditional path planning algorithm in dynamic obstacle environment, a path planning control algorithm based on improved artificial potential field method is proposed. Firstly, a virtual potential field detection circle model (VPFDCM) with adjustable radius is proposed to detect the "minimum trap" formed by the repulsion field of obstacles in advance. And the motion model of unmanned vehicle is established. Combined with the improved reinforcement learning algorithm based on Long Short-Term Memory(LSTM), the radius of virtual potential field detection circle is adjusted to achieve effective avoidance of dynamic obstacles, The reliable online collision free path planning of unmanned vehicle in semi closed dynamic obstacle environment is realized. Finally, the reliability and robustness of the algorithm are verified by MATLAB simulation. The simulation results show that the improved artificial potential field method can effectively solve the problem of unmanned vehicle falling into the "minimum trap" and improve the reliability of unmanned vehicle movement. Compared with the traditional artificial potential field method, the improved artificial potential field method can achieve more than 90% success rate in obstacle avoidance.

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“…As a variant of recurrent neural network (RNN), LSTM has a long-term memory function that is suitable for processing important events with long intervals and delays in time series. Therefore, the neural network structure, which is primarily composed of LSTM units with memory functions, can make decisions based on previous states to adapt to various running scenarios (Guo et al, 2021). LSTM has been widely used in issues related to sequential data such as natural language processing (NLP), voice recognition, and time series analysis (Sezer & Ozbayoglu, 2018).…”

Section: Machine-deep Learning Algorithmsmentioning

confidence: 99%

Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit

Chen

Sharifrazi

Liang

et al. 2022

Engineering Applications of Computational Fluid Mechanics

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Streamlined weirs, as a nature-inspired type of weirs, has gained tremendous attention among hydraulic engineers mainly due to their well-known performance with high discharge coefficient. Computational fluid dynamic (CDF) is considered as a robust tool to predict discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modelling techniques, as an alternative to CFD simulation, to predict discharge coefficient based on an experimental dataset. To this end, after splitting the dataset by k-fold cross-validation technique, the performance assessment of classical and hybrid machine-deep learning (ML-DL) algorithms is undertaken. Amongst ML techniques, linear regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and decision tree (DT) algorithms are studied . In the context of DL, long short-term memory (LSTM), convolutional neural network (CNN), gated recurrent unit (GRU) and their hybrid forms such as LSTM-GRU, CNN-LSTM and CNN-GRU techniques are compared by different error metrics. It is found that the proposed three-layer hierarchical DL algorithm consisting of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized by LR method (i.e., LR-CGRU), leads to lower error metrics. This paper paves the way for data-driven modelling of streamlined weirs.

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A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

Cited by 18 publications

References 39 publications

Real-Time UAV Path Planning Based on LSTM Network

Real-Time UAV Path Planning Based on LSTM Network

Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

Accurate discharge coefficient prediction of streamlined weirs by coupling linear regression and deep convolutional gated recurrent unit

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