Intelligent systems with even the bare minimum of sophistication require extensive computational power and complex processing units. At the same time, small insects like flies are adept at visual navigation, target pursuit, motionless hovering flight, and obstacle avoidance. Thus, biology provides engineers with an unconventional approach to solve complicated engineering design problems. Computational models of the neuronal architecture of the insect brain can provide algorithms for the development of software and hardware to accomplish sophisticated visual navigation tasks. In this research, we investigate biologically-inspired collision avoidance models primarily based on visual motion. We first present a comparative analysis of two leading collision avoidance models hypothesized in the insect brain. The models are simulated and mathematically analyzed for collision and non-collision scenarios. Based on this analysis it is proposed that along with the motion information, an estimate of distance from the obstacle is also required to reliably avoid collisions. We present models with tracking capability as solutions to this problem and show that tracking indirectly computes a measure of distance. We present a camera-based implementation of the collision avoidance models with tracking. The camera-based system was tested for collision and non-collision scenarios to verify our simulation claims that tracking improves collision avoidance. Next, we present a direct approach to estimate the distance from an obstacle by utilizing non-directional speed. We describe two simplified non-directional speed estimation models: the non-directional multiplication (ND-M) sensor, and the non-directional summation (ND-S) sensor. We also analyze the mathematical basis of their speed sensitivity. An analog VLSI chip was designed and fabricated to implement these models in silicon. The chip was fabricated in a 0.18 mum process and its characterization results are reported here. As future work, the tracking algorithm and the collision avoidance models may be implemented as a sensor chip and used for autonomous navigation by intelligent systems.Laptop S1 S2 P1 P2 W Ob Servo Control Board Power Supply (a) (b) Figure 4.1. Experimental setup of the collision detection experiment. (a) Diagram of the setup . Two pulleys (P1 and P2) control the trajectory of the obstacle (Ob), a blueanbsp;...
|Title||:||Biomimetic Visual Navigation Architectures for Autonomous Intelligent Systems|
|Publisher||:||ProQuest - 2007|