Global Multi-Phase Path Planning Through High-Level Reinforcement Learning
In this paper, we introduce the <italic>Global Multi-Phase Path Planning</italic> (<monospace><inline-formula><tex-math notation="LaTeX">$GMP^{3}$</tex-math></inline-formula></monospace>) algorithm in planner problems, which computes fast and...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Open Journal of Control Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10613437/ |
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| Summary: | In this paper, we introduce the <italic>Global Multi-Phase Path Planning</italic> (<monospace><inline-formula><tex-math notation="LaTeX">$GMP^{3}$</tex-math></inline-formula></monospace>) algorithm in planner problems, which computes fast and feasible trajectories in environments with obstacles, considering physical and kinematic constraints. Our approach utilizes a Markov Decision Process (MDP) framework and high-level reinforcement learning techniques to ensure trajectory smoothness, continuity, and compliance with constraints. Through extensive simulations, we demonstrate the algorithm's effectiveness and efficiency across various scenarios. We highlight existing path planning challenges, particularly in integrating dynamic adaptability and computational efficiency. The results validate our method's convergence guarantees using Lyapunov’s stability theorem and underscore its computational advantages. |
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| ISSN: | 2694-085X |