UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance

2023/02/21 by Mahya Ramezani, Hamed Habibi, Ramezani, Mahya +5 · 1 citation
Computer Science · #Adaptive Dynamic Programming Control #Artificial Intelligence (cs.AI) #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2302.10669

openalex publication_date 2023/02/21 · openalex created_date 2023/02/24 · openalex updated_date 2026/08/04

Abstract

In this paper, we tackle the problem of Unmanned Aerial (UA V) path planning in complex and uncertain environments by designing a Model Predictive Control (MPC), based on a Long-Short-Term Memory (LSTM) network integrated into the Deep Deterministic Policy Gradient algorithm. In the proposed solution, LSTM-MPC operates as a deterministic policy within the DDPG network, and it leverages a predicting pool to store predicted future states and actions for improved robustness and efficiency. The use of the predicting pool also enables the initialization of the critic network, leading to improved convergence speed and reduced failure rate compared to traditional reinforcement learning and deep reinforcement learning methods. The effectiveness of the proposed solution is evaluated by numerical simulations.

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