Paper Info
Title
Efficient and resilient micro air vehicle flapping wing gait evolution for hover and trajectory control.
Abstract
This paper deploys a recently proposed, biologically inspired, on-line, search-based optimization technique called Selective Evolutionary Generation Systems (SEGS) for control purposes; here, to evolve Micro Air Vehicle (MAV) flapping wing gaits in changing flight conditions to maintain hovering flight and track trajectories in unsteady airflow. The SEGS technique has several advantages, including: (1) search-efficiency, by optimally trading off prior search space information for search effort savings as quickly as possible in dynamic environments; (2) model-independence, as in biology, avoiding biases induced by built-in models rendered incorrect by environment changes; and (3) resilience, through sufficiency for stochastic behavior that is itself sufficient for responsiveness to search-objective variations caused by environment fluctuations. This work presents the first approach that can simultaneously evolve optimal MAV flapping wing gaits efficiently and resiliently, adapt on-line, and, via model-independence, allow feedback from either experimental sensors or alternate external models (affording control versatility for hover or forward flight, unsteady or quasi-steady aerodynamics, and any dynamics or wing kinematics). Performance benchmarks are also provided. Because the (1+1)-Evolution Strategy (ES) and the Canonical Genetic Algorithm with Fitness Proportional Selection (CGAFPS) are two SEGS special extreme cases, an additional comparison showcases SEGS possession of both (1+1)-ES computational speed and CGAFPS resilience. HighlightsA bioinspired, search-efficient, tunable optimization scheme is adapted for control.Micro Air Vehicle (MAV) flapping wing gaits are evolved on-line, model-independently.Scheme properties are benchmarked in a case study of evolution for MAV hover control.Scheme speed and responsiveness compare favorably to related evolutionary methods.A second study attains MAV trajectory control in unsteady flow with little computing.
Year
DOI
Venue
2016
10.1016/j.engappai.2016.05.001
Eng. Appl. of AI
Keywords
Field
DocType
Micro Air Vehicles (MAVs),Flapping wing gait evolution,Selective evolutionary generation,Hovering flight,Trajectory tracking
Wing,Mathematical optimization,Kinematics,Gait,Simulation,Computer science,Control theory,Airflow,Flapping wing,Micro air vehicle,Genetic algorithm,Aerodynamics
Journal
Volume
Issue
ISSN
54
C
0952-1976
Citations 
PageRank 
References 
1
0.39
19
Authors
2
Name
Order
Citations
PageRank
Amor a. Menezes1144.73
Pierre T. Kabamba25817.07