Abstract
In order to solve the problem of frequent deceleration of unmanned vehicles when approaching obstacles, this article uses a Deep Q-Network (DQN) and its extension, the Double Deep Q-Network (DDQN), to develop a local navigation system that adapts to obstacles while maintaining optimal speed planning. By integrating improved reward functions and obstacle angle determination methods, the system demonstrates significant enhancements in maneuvering capabilities without frequent decelerations. Experiments conducted in simulated environments with varying obstacle densities confirm the effectiveness of the proposed method in achieving more stable and efficient path planning.
Abstract (translated)
为了解决无人机在接近障碍物时频繁减速的问题，本文使用深度 Q 网络（DQN）及其扩展，双深度 Q 网络（DDQN），开发了一种局部导航系统，能够在保持最优速度规划的同时适应障碍物。通过整合改进的奖励函数和障碍物角度确定方法，系统在不需要频繁减速的情况下显示出显著的操纵能力提升。在具有不同障碍物密度的模拟环境中进行的实验证实了所提出方法在实现更稳定和高效的路径规划方面的有效性。
URL
https://arxiv.org/abs/2404.17379
