A vision-based terrain morphology estimation model inspired by the avian hippocampus

AbstractHoming pigeons are known for their ability to return home after being released from a location spanning up to hundreds of miles. They make use of detected visual features in the environment, the earth׳s magnetic field as well as using the hippocampus region of the brain to construct spatial maps of the environment. This is unlike present day UAVs that rely on GPS and radio/satellite communications with a ground station, both of which might not be available during a major disaster scenario such as a solar flare.In this paper, we take inspiration from the avian hippocampus and develop a preliminary model for estimating a terrain׳s morphology using visually detected features on the terrain. This could then be used to localise a portable micro-UAV during a demining task for humanitarian purposes in third world countries affected by buried land mines from previous wars. Our goal is that in future, the presented model and algorithm in this work would enable effective coverage of an affected area using the visual information obtained from the environment

Visual Imaging of Invisible Hazardous Substances Using Bacterial Inspiration

Providing a visual image of a hazardous substance such as nerve gas or nuclear radiation using multiple robotic agents could be very useful particularly when the substance is invisible. Such visual representation could show where the hazardous substance concentration is highest through the deployment of a higher density of robotic agents to that area enabling humans to avoid such areas. We present an algorithm that is capable of doing the aforementioned with very minimal cost when compared with other techniques such as Voronoi partition methods. Using a mathematical proof, we show that the algorithm would always converge to the distribution of a spatial quantity under investigation. The mathematical model of the bacterium as developed by Berg and Brown is used in this paper, and through simulations and physical experiments, we show that a controller based upon the model is capable of being used to visually represent an invisible spatial hazardous substance using simplistic agents with the future possibility of the same algorithm being used to track a rapidly changing spatiotemporal substance. We believe that the algorithm has this potential because of its low communication and computational needs. © 2013 IEEE