Title :
A Reinforcement Learning Approach to Lift Generation in Flapping MAVs: Experimental Results
Author :
Motamed, Mehran ; Yan, Joseph
Author_Institution :
Motion Metrics Int. Corp., Vancouver, BC
Abstract :
We proposed an RL framework for control of flapping-wing MAVs (2006). The algorithm has been discussed and simulation results using a quasi-steady model showed initial promise. In this paper, the results from an experiment on a Drosophila-based dynamically scaled model are presented and are used to verify the control framework. Moreover, a comparison between a biological Drosophila melanogaster and the experimental results shows the actual possibility of employing the proposed approach to MAV control problem
Keywords :
aerodynamics; aerospace robotics; learning (artificial intelligence); microrobots; mobile robots; robot dynamics; Drosophila-based dynamically scaled model; biological Drosophila melanogaster; flapping microaerial vehicles; lift generation; quasisteady model; reinforcement learning; Aerodynamics; Biological system modeling; Computational fluid dynamics; Force measurement; Insects; Learning; Motion control; Prototypes; Robotics and automation; Solid modeling;
Conference_Titel :
Robotics and Automation, 2007 IEEE International Conference on
Conference_Location :
Roma
Print_ISBN :
1-4244-0601-3
Electronic_ISBN :
1050-4729
DOI :
10.1109/ROBOT.2007.363076