Optimizing Energy Efficiency of a Flapping Robotic Bird Through Application of Evolutionary Algorithms

Recent advances in servo motors have enabled construction of a new low cost class of flapping wing robots. These robotic, bird-sized flapping wing vehicles are capable of independently controlling wing position. In contrast, most previously-developed flapping wing mechanisms rely on mechanical linkages to oscillate the wings and can only control the flapping frequency. This new class of servo-based robotic birds allows for minimization of the number of necessary actuators and can provide for more degrees of freedom, eliminating the need for additional control surfaces. As with many other flapping wing vehicles, power consumption is of great concern, since it is the limiting operational factor of flight duration. In this paper, we investigate the use of evolutionary algorithms to optimize the flapping and gliding patterns of a robotic bird. For gliding, we maximize the lift to drag ratio while minimizing the aerodynamic moments and side force. For flapping, we maximize the lift while minimizing the drag and power consumption by modulating the frequency of the up and down wing strokes. In order to allow on-board learning, we modify an existing design to have similar aero-surface area and significantly reduced weight, accommodating an increased computational payload.