Hierarchical path planner for unknown space exploration using reinforcement learning-based intelligent frontier selection

Fan, Jie; Zhang, Xudong; Zou, Yuan

doi:10.1016/j.eswa.2023.120630

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Reinforcement learning (RL) is a type of artificial intelligence inspired by how we learn through trial and error [9]. The agent takes actions, receives rewards or penalties based on the outcomes, and learns to choose actions that maximize the long-term rewards.…”

Section: Rl-based Ems Modelingmentioning

confidence: 99%

Reinforcement learning-based energy management for hybrid electric vehicle enhanced by velocity prediction

Luo,

Bai,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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This paper proposes a novel approach for optimal energy management of engine-battery hybrid electric systems. It addresses two limitations in existing research, namely inaccurate speed prediction and low control accuracy. The proposed method integrates Markov theory and clustering theory to achieve high-precision prediction of future velocity under diverse driving styles. Furthermore, a learning-based energy management strategy is developed using a deep deterministic strategy gradient algorithm. This strategy considers the predicted velocity and aims to minimize engine fuel consumption, battery state fluctuations, and engine start-stop occurrences. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance under specific operating conditions.

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Section: Rl-based Ems Modelingmentioning

confidence: 99%

Reinforcement learning-based energy management for hybrid electric vehicle enhanced by velocity prediction

Luo,

Bai,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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“…Similarly, Wang et al introduced an off-road path-planning approach based on deep RL, training the agent within a low-dimensional simulator constructed using occupancy maps [23]. Furthermore, Fan et al proposed a hierarchical RL-based path planner for exploring unknown spaces, utilizing lidar readings alongside iteratively generated occupancy maps as observations for the RL agent [24]. However, while employing original occupancy maps or sensor observation data as inputs for UGV path planning facilitates the preservation of environmental information to a large extent, it also presents challenges.…”

Section: Introductionmentioning

confidence: 99%

Highly Self-Adaptive Path-Planning Method for Unmanned Ground Vehicle Based on Transformer Encoder Feature Extraction and Incremental Reinforcement Learning

Zhang,

Fan,

Zhou

et al. 2024

Machines

Self Cite

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Path planning is an indispensable component in guiding unmanned ground vehicles (UGVs) from their initial positions to designated destinations, aiming to determine trajectories that are either optimal or near-optimal. While conventional path-planning techniques have been employed for this purpose, planners utilizing reinforcement learning (RL) exhibit superior adaptability within exceedingly complex and dynamic environments. Nevertheless, existing RL-based path planners encounter several shortcomings, notably, redundant map representations, inadequate feature extraction, and limited adaptiveness across diverse environments. In response to these challenges, this paper proposes an innovative and highly self-adaptive path-planning approach based on Transformer encoder feature extraction coupled with incremental reinforcement learning (IRL). Initially, an autoencoder is utilized to compress redundant map representations, providing the planner with sufficient environmental data while minimizing dimensional complexity. Subsequently, the Transformer encoder, renowned for its capacity to analyze global long-range dependencies, is employed to capture intricate correlations among UGV statuses at continuous intervals. Finally, IRL is harnessed to enhance the path planner’s generalization capabilities, particularly when the trained agent is deployed in environments distinct from its training counterparts. Our empirical findings demonstrate that the proposed method outperforms traditional uniform-sampling-based approaches in terms of execution time, path length, and trajectory smoothness. Furthermore, it exhibits a fivefold increase in adaptivity compared to conventional transfer-learning-based fine-tuning methodologies.

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Improving policy training for autonomous driving through randomized ensembled double Q-learning with Transformer encoder feature evaluation

Fan,

Zhang,

Zou

et al. 2024

Applied Soft Computing

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Hierarchical path planner for unknown space exploration using reinforcement learning-based intelligent frontier selection

Cited by 7 publications

References 34 publications

Reinforcement learning-based energy management for hybrid electric vehicle enhanced by velocity prediction

Reinforcement learning-based energy management for hybrid electric vehicle enhanced by velocity prediction

Highly Self-Adaptive Path-Planning Method for Unmanned Ground Vehicle Based on Transformer Encoder Feature Extraction and Incremental Reinforcement Learning

Improving policy training for autonomous driving through randomized ensembled double Q-learning with Transformer encoder feature evaluation

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