This paper presents an incremental learning method and system for autonomous robot navigation. The range finder laser sensor and online deep reinforcement learning are utilized for generating the navigation policy, which is effective for avoiding obstacles along the robot’s trajectories as well as for robot’s reaching the destination. An empirical experiment is conducted under simulation and real-world settings. Under the simulation environment, the results show that the proposed method can generate a highly effective navigation policy (more than 90% accuracy) after only 150k training iterations. Moreover, our system has slightly outperformed deep-Q, while having considerably surpassed Proximal Policy Optimization, two recent state-of-the art robot navigation systems. Finally, two experiments are performed to demonstrate the feasibility and effectiveness of our robot’s proposed navigation system in real-time under real-world settings.

本文提出了一种用于自主机器人导航的增量学习方法和系统。利用测距仪激光传感器和在线深度强化学习来生成导航策略，这对于避免沿机器人轨迹的障碍以及机器人到达目的地是有效的。在模拟和真实环境下进行了实证实验。在仿真环境下，结果表明，所提出的方法仅需150k次训练迭代就可以生成高效的导航策略（准确率超过90％）。此外，我们的系统在性能上略胜过Deep-Q，同时又大大超过了近端策略优化（最近的两个最先进的机器人导航系统）。最后，