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.
Cuong Pham received the B.S. degree from Vietnam National University, in 1998, the M.S. degree from New Mexico State University, USA, in 2005, and the Ph.D. degree from Newcastle University, U.K., in 2012, all majors in computer science. He was a Visiting Professor with the University of Palermo, Italy and a Marie Curie Research Fellow with Philips Research, Eindhoven, The Netherlands. He is currently an Associate Professor of computer science with the Posts and Telecommunications Institute of Technology (PTIT) and a Visiting Research Scientist with VinAI Research. His main research interests are ubiquitous computing, wearable computing, human activity recognition, and machine learning/deep learning.
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