Evolving Vision-Based Flying Robots

Abstract

Abstract. We describe a new experimental approach whereby an indoor flying robot evolves the ability to navigate in a textured room using only visual information and neuromorphic control. The architecture of a spiking neural circuit, which is connected to the vision system and to the motors, is genetically encoded and evolved on the physical robot without human intervention. The flying robot consists of a small wireless airship equipped with a linear camera and a set of sensors used to measure its performance. Evolved spiking circuits can manage to fly the robot around the room by exploiting a combination of visual features, robot morphology, and interaction dynamics.

Aggregate Selection in Evolutionary Robotics

“…In [47] an indoor floating robotic blimp equipped with a camera and placed in a small room with bar-code-like markings on the walls was evolved to produce motion and wall avoidance. An essentially aggregate fitness function was used that averaged magnitude of velocity over each trial period.…”

From Wheels to Wings with Evolutionary Spiking Circuits

“…The fitness is proportional to the clockwise rotation of the anemometer, which is an estimate of the forward navigation of the blimp. A preliminary set of experiments indicated that artificial evolution generates in about 20 generations spiking controllers that drive the blimp around the room [41]. Robots equipped with evolved controllers did not totally avoid hitting walls since they are not explicitly asked to do so.…”

Simulation scheme for quadricopter control with labview and x-plane

“…Among the various UAVs platforms such as: fixed wing ( Teo et al (2004) and blimp Zufferey et al (2002) stand out the quadricopters. They are aircrafts heavier than air that have the vertical take-off and landing feature (VTOL).…”