Paper accepted for presentation at one of the most prestigious conferences. Ranked 21st highest impact of all computer science conferences worldwide. H-index of 60. Over 500 attendees, low acceptance rate.
Neural Networks evolving for Quadruped Robotic Locomotion Control
Nature Inspired Robot Swarm Cooperative Retrieval
In nature bees and leaf-cutter ants communicate to improve cooperation during food retrieval. This research aims to model communication in a swarm of autonomous robots. When food is identified robot communication is emitted within a limited range. Other robots within the range receive the communication and learn of the location and size of the food source. The simulation revealed that communication improved the rate of cooperative food retrieval tasks. However a counter-productive chain reaction can occur when robots repeat communications from other robots causing cooperation errors. This can lean to a large number of robots travelling towards the same food source at the same time. The food becomes depleted, before some robots have arrived. Several robots continue to communicate food presence, before arriving at the food source to find it gone. Nature-inspired communication can enhance swarm behaviour without requiring a central controller and may be useful in autonomous drones, vehicles or robotics.
Figure 1: (a) Finite state machine behaviour control (b) Screenshot of simulated swarm communication (c) Graph of node connections between robots
Download source code and windows .exe executable here
Face and skin detection in images and video (2005)
This diagram shows the workings of the Vertical Profile Face Detector