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

arXiv: https://arxiv.org/abs/2302.10669 · HTML: https://mlsanity.com/paper/2302.10669/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Data-driven Economic NMPC using Reinforcement Learning](https://mlsanity.com/paper/1904.04152/) (2019)
- [MPC-based Reinforcement Learning for Economic Problems with Application to Battery Storage](https://mlsanity.com/paper/2104.02411/) (2021)
- [MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles](https://mlsanity.com/paper/2106.08634/) (2021)
- [Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments](https://mlsanity.com/paper/1912.05684/) (2019)

## Built on by (most cited first, 7 of 7)

- [Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay](https://mlsanity.com/paper/2402.18487/) (2024)
- [Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05996/) (2025)
- [MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation](https://mlsanity.com/paper/2509.21045/) (2025)
- [A Comparison of Reinforcement Learning and Optimal Control Methods for Path Planning](https://mlsanity.com/paper/2604.12628/) (2026)
- [3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots](https://mlsanity.com/paper/2605.12689/) (2026)
- [Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach](https://mlsanity.com/paper/2606.07855/) (2026)
- [Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training](https://mlsanity.com/paper/2604.26833/) (2026)

## Abstract (arXiv)

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.