Paper presented at Unmanned Aircraft Systems Conference

The team of SESAME researchers at University of Luxembourg has presented a technical paper at the International Conference on Unmanned Aircraft Systems (ICUAS) held in Warsaw, Poland. The paper titled: UAV Path Planning Employing MPC-Reinforcement Learning Method Considering Collision Avoidance addresses the problem of Unmanned Aerial (UAV) 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.