Technical Note: \cal Q-Learning
Machine Learning
Learning in embedded systems
Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dynamic Motion Planning for Mobile Robots Using Potential Field Method
Autonomous Robots
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Line of sight robot navigation toward a moving goal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Target tracking using a hierarchical grey-fuzzy motion decision-making method
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy target tracking control of autonomous mobile robots by using infrared sensors
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
In this paper, a reinforcement learning method called DAQL is proposed to solve the problem of seeking and homing onto a fast maneuvering target, within the context of mobile robots. This Q-learning based method considers both target and obstacle actions when determining its own action decisions, which enables the agent to learn more effectively in a dynamically changing environment. It particularly suits fast-maneuvering target cases, in which maneuvers of the target are unknown a priori. Simulation result depicts that the proposed method is able to choose a less convoluted path to reach the target when compared to the ideal proportional navigation (IPN) method in handling fast maneuvering and randomly moving target. Furthermore, it can learn to adapt to the physical limitation of the system and do not require specific initial conditions to be satisfied for successful navigation towards the moving target.