无人机
强化学习
卡尔曼滤波器
计算机科学
扩展卡尔曼滤波器
人工智能
计算机视觉
人机交互
航空学
工程类
遗传学
生物
作者
Francesco Marino,Giorgio Guglieri
出处
期刊:Aerospace
[Multidisciplinary Digital Publishing Institute]
日期:2024-05-15
卷期号:11 (5): 395-395
被引量:1
标识
DOI:10.3390/aerospace11050395
摘要
Autonomous drones offer immense potential in dynamic environments, but their navigation systems often struggle with moving obstacles. This paper presents a novel approach for drone trajectory planning in such scenarios, combining the Interactive Multiple Model (IMM) Kalman filter with Proximal Policy Optimization (PPO) reinforcement learning (RL). The IMM Kalman filter addresses state estimation challenges by modeling the potential motion patterns of moving objects. This enables accurate prediction of future object positions, even in uncertain environments. The PPO reinforcement learning algorithm then leverages these predictions to optimize the drone’s real-time trajectory. Additionally, the capability of PPO to work with continuous action spaces makes it ideal for the smooth control adjustments required for safe navigation. Our simulation results demonstrate the effectiveness of this combined approach. The drone successfully navigates complex dynamic environments, achieving collision avoidance and goal-oriented behavior. This work highlights the potential of integrating advanced state estimation and reinforcement learning techniques to enhance autonomous drone capabilities in unpredictable settings.
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