A μ analysis-based, controller-synthesis framework for robust bioinspired visual navigation in less-structured environments.
Safe, autonomous navigation by aerial microsystems in less-structured environments is a difficult challenge to overcome with current technology. This paper presents a novel visual-navigation approach that combines bioinspired wide-field processing of optic flow information with control-theoretic tools for synthesis of closed loop systems, resulting in robustness and performance guarantees. Structured singular value analysis is used to synthesize a dynamic controller that provides good tracking performance in uncertain environments without resorting to explicit pose estimation or extraction of a detailed environmental depth map. Experimental results with a quadrotor demonstrate the vehicle's robust obstacle-avoidance behaviour in a straight line corridor, an S-shaped corridor and a corridor with obstacles distributed in the vehicle's path. The computational efficiency and simplicity of the current approach offers a promising alternative to satisfying the payload, power and bandwidth constraints imposed by aerial microsystems.
Autonomous Vehicles Laboratory, Department of Aerospace Engineering, University of Maryland, College Park 20742, USA.
J
J
Keshavan
G
G
Gremillion
H
H
Escobar-Alvarez
J S
JS
Humbert
D000401:Aircraft / Q000295:instrumentation*; D000818:Animals; D032701:Biomimetics / Q000295:instrumentation* / Q000379:methods; D003198:Computer Simulation; D017076:Computer-Aided Design; D004867:Equipment Design; D019544:Equipment Failure Analysis; D056228:Feedback, Sensory / Q000502:physiology; D005426:Flight, Animal / Q000502:physiology*; D008954:Models, Biological; D057849:Optic Flow / Q000502:physiology*; D012371:Robotics / Q000295:instrumentation*; D065854:Spatial Navigation / Q000502:physiology*