Multipath Planning Acceleration Method With Double Deep R-Learning Based on a Genetic Algorithm

Palacios-Morocho, Elizabeth; Inca, Saul; Monserrat, José F.

doi:10.1109/tvt.2023.3277981

IEEE Trans. Veh. Technol.

2023

DOI: 10.1109/tvt.2023.3277981

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Multipath Planning Acceleration Method With Double Deep R-Learning Based on a Genetic Algorithm

Elizabeth Palacios-Morocho

Saul Inca

José F. Monserrat

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Cited by 4 publications

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“…Path planning algorithms can be divided into global and local path planning according to the amount of a priori information [3]. In 1975, Professor J. Holland [4] first proposed the genetic algorithm. The genetic algorithm is a method to find the optimal solution by simulating the natural biological evolution.…”

mentioning

confidence: 99%

A path planning algorithm fusion of obstacle avoidance and memory functions

Zheng,

Li,

Zhu

et al. 2023

Cognitive Comp and Systems

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In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short‐Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method‐Long Short Term Memory Deep Deterministic Policy Gradient (APF‐LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM‐DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions.

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confidence: 99%

A path planning algorithm fusion of obstacle avoidance and memory functions

Zheng,

Li,

Zhu

et al. 2023

Cognitive Comp and Systems

View full text Add to dashboard Cite

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Data Homogenization Method for Heterogeneous Sensors Applied to Reinforcement Learning

Palacios-Morocho,

López-Muńoz,

Costán

et al. 2023

IEEE Access

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Enhancing Cooperative Multi-Agent Systems With Self-Advice and Near-Neighbor Priority Collision Control

Palacios-Morocho¹,

Inca²,

Monserrat³

2024

IEEE Trans. Intell. Veh.

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Multipath Planning Acceleration Method With Double Deep R-Learning Based on a Genetic Algorithm

Cited by 4 publications

References 45 publications

A path planning algorithm fusion of obstacle avoidance and memory functions

A path planning algorithm fusion of obstacle avoidance and memory functions

Data Homogenization Method for Heterogeneous Sensors Applied to Reinforcement Learning

Enhancing Cooperative Multi-Agent Systems With Self-Advice and Near-Neighbor Priority Collision Control

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