Autonomous blimp control using reinforcement learning
This paper presents a new approach for navigation control of an autonomous blimp using an intelligent reinforcement learning algorithm. Compared to the traditional model based control methods, this control strategy does not require a dynamic model of the blimp, which provides huge advantage in many practical situations where the blimp system model is either hard to acquire or too complicated to apply. As the blimp will acquire a range of the pilot skills through a learning and rewarding mechanism during actual navigation trials, it can automatically account for the environmental changes during the navigation. The simulation data generated from a Webots Robotics Simulator (WRS) demonstrate satisfactory results for planar steering motion control. Reinforcement learning based blimp control is shown to be a promising and effective solution for autonomous navigation tasks.
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