UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance
This addresses path planning for UAVs in uncertain settings, but it appears incremental as it builds on existing MPC and reinforcement learning techniques.
The paper tackles UAV path planning in complex environments by integrating an LSTM-based MPC into DDPG, resulting in improved convergence speed and reduced failure rate compared to traditional methods.
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.