Abstract
This paper presents a novel reinforcement learning (RL) approach for a tethered drone to follow a predefined three-dimensional trajectory in a GPS-denied environment. The adopted Q-learning strategy determines high-level actions using raw observations from the onoard accelerometers, gyros, and altimeter, which facilitates a low-level proportional-integral-derivative (PID) controller to drive the drone through the desired waypoints on a reference trajectory. The effectiveness of the proposed approach is demonstrated in a simulated environment.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
