mlsanityData to 14 Oct 2026

Multi-AGV Path Planning Method via Reinforcement Learning and Particle Filters

arXiv 2403.18236 ↗First posted 27 Mar 2024cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Thanks to its robust learning and search stabilities,the reinforcement learning (RL) algorithm has garnered increasingly significant attention and been exten-sively applied in Automated Guided Vehicle (AGV) path planning. However, RL-based planning algorithms have been discovered to suffer from the substantial variance of neural networks caused by environmental instability and significant fluctua-tions in system structure. These challenges manifest in slow convergence speed and low learning efficiency. To tackle this issue, this paper presents a novel multi-AGV path planning method named Particle Filters - Double Deep Q-Network (PF-DDQN)via leveraging Particle Filters (PF) and RL algorithm. Firstly, the proposed method leverages the imprecise weight values of the network as state values to formulate thestate space equation.Subsequently, the DDQN model is optimized to acquire the optimal true weight values through the iterative fusion process of neural networksand PF in order to enhance the optimization efficiency of the proposedmethod. Lastly, the performance of the proposed method is validated by different numerical simulations. The simulation results demonstrate that the proposed methoddominates the traditional DDQN algorithm in terms of path planning superiority andtraining time indicator by 92.62% and 76.88%, respectively. Therefore, the proposedmethod could be considered as a vital alternative in the field of multi-AGV path planning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 34 references that are in the map. First posted on arXiv 27 Mar 2024.

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10 of 34 references.

References · 10

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  3. 2015DRAW: A Recurrent Neural Network For Image Generationcited by 191
  4. 2018Deep Variational Reinforcement Learning for POMDPscited by 125
  5. 2020Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehiclescited by 110
  6. 2017Variational Sequential Monte Carlocited by 95
  7. 2018Differentiable Particle Filters: End-to-End Learning with Algorithmic Priorscited by 59
  8. 2023Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfindingcited by 6
  9. 2017Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospectscited by 5
  10. 2023Multiparticle Kalman filter for object localization in symmetric environmentscited by 2

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  4. 2024Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning
  5. 2024Research on Autonomous Robots Navigation based on Reinforcement Learning
  6. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  7. 2026Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving
  8. 2024Deep Reinforcement Learning for Mobile Robot Path Planning