Special Issue for Artificial Intellligence Evolution
“Reinforcement Learning in Mobile Robot Navigation”
Navigation is a fundamental problem of mobile robots, which include unmanned vehicles, aerial vehicles, ships, etc. So far, Reinforcement Learning (RL) has witnessed significant success in mobile robot navigation employing its strong experience learning abilities. The main principle behind RL is to let agents learn how to act optimally to maximize the environmental reward generated in response to its actions. Such principle endows RL unique advantages in navigating mobile robots dynamically through online exploration and interactions with the environment. As a result, there is an increasing trend of employing RL to mobile robot navigation. Under this situation, a special issue on the advancements in reinforcement learning for mobile robot navigation will help update the current level of the state-of-the-art approaches, technologies, and applications in this direction. To this end, this special issue focuses on promoting the development of RL-based navigation methods, including theoretic studies, algorithm development, applications, and experimental validations. Through this Issue, we expect to encourage collaboration between researchers from diverse fields such as computational intelligence, control systems, reinforcement learning, mobile robots, and intelligent transportation.
Scope of the Special Issue
Artificial Intelligence Evolution invites submissions on all topics of RL based mobile robot navigation methods, including but not limited to:
lNovel reinforcement learning methods for navigation/path planning
lNovel deep reinforcement learning methods for navigation/path planning
lFuzzy reinforcement learning methods for navigation/path planning
lReinforcement learning based methods for navigation in unknown environment/uncertain environment/dynamic environment/indoor environment
lReinforcement learning based methods for multi-robot navigation
lReinforcement learning based methods for social navigation
lReinforcement learning based methods for large-scale mobile robot navigation
lReinforcement learning based methods for obstacle avoidance
lReinforcement learning based methods for 3D navigation/path planning
Prof. Qiang Yang
Nanjing University of Information Science and Technology, China
Dr. Krishan Kumar
Head of the Department, Computer Science & Engineering, National Institute of Technology Uttarakhand, India
lManuscripts can be submitted directly to the official mailbox (firstname.lastname@example.org) or online via http://ojs.wiserpub.com/index.php/AIE/about/submissions.
All submissions will go through the regular double-blind peer-review process and follow standard norms after they pass the preliminary review by the panel. Before submitting a manuscript, please check the Submission Guidelines.
lPublication fee: free of APC or any other costs during the whole editorial process.
Queries or any other matters not expressly, please contact the journal editor of AIE (email@example.com).