Special Issue for Artificial Intellligence Evolution

 “Reinforcement Learning in Mobile Robot Navigation”

lDescription

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

Guest Editor

Prof. Qiang Yang

Nanjing University of Information Science and Technology, China

Email: mmmyq@126.com

Website: https://mmmyq2.wixsite.com/qiangyang

Dr. Krishan Kumar

Head of the Department, Computer Science & Engineering, National Institute of Technology Uttarakhand, India

Email: kkberwal@nituk.ac.in

Website: https://publons.com/researcher/3350627/krishan-kumar/

Manuscript submission

lManuscripts can be submitted directly to the official mailbox (editorial-aie@wiserpub.com) 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 (editorial-aie@wiserpub.com).