In this study, we present two distinct approaches within the realm of Deep Reinforcement Learning (Deep-RL) aimed at enhancing mapless navigation for a ground-based mobile robot. The research methodology primarily involves a comparative analysis between a Deep-RL strategy grounded in the foundational Deep Q-Network (DQN) algorithm, and an alternative approach based on the Double Deep Q-Network (DDQN) algorithm. The agents in these approaches leverage 24 measurements from laser range sampling, coupled with the agent's positional differentials and orientation relative to the target. This amalgamation of data influences the agents' determinations regarding navigation, ultimately dictating the robot's velocities. By embracing this parsimonious sensory framework as proposed, we successfully showcase the training of an agent for proficiently executing navigation tasks and adeptly circumventing obstacles. Notably, this accomplishment is attained without a dependency on intricate sensory inputs like those inherent to image-centric methodologies. The proposed methodology is evaluated in three different real environments, revealing that Double Deep structures significantly enhance the navigation capabilities of mobile robots compared to simple Q structures.

翻译：本研究提出了两种基于深度强化学习（Deep-RL）的方法，旨在增强地面移动机器人的无地图导航能力。研究方法主要涉及基于基础深度Q网络（DQN）算法的深度强化学习策略与基于双重深度Q网络（DDQN）算法的替代策略之间的比较分析。两种策略中的智能体利用激光测距采样的24个测量值，并结合智能体相对于目标的位置差和方向角。这些数据的融合影响智能体对导航的决策，最终决定机器人的速度。通过采用所提出的简约感知框架，我们成功展示了训练智能体高效执行导航任务并灵活规避障碍物的能力。值得注意的是，这一成就未依赖于图像中心方法中固有的复杂感知输入。所提方法在三种不同的真实环境中进行了评估，结果表明，与简单的Q结构相比，双重深度结构显著增强了移动机器人的导航能力。