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

TL;DR

This paper introduces a novel UAV path planning method combining MPC and reinforcement learning, utilizing LSTM networks for better collision avoidance and efficiency in complex environments.

Contribution

It proposes an LSTM-based MPC integrated with DDPG, enhancing robustness, convergence speed, and collision avoidance in UAV path planning.

Findings

Improved convergence speed over traditional methods

Reduced failure rate in path planning

Enhanced robustness in complex environments

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.