This presentation will include… Introduction to Digital Robotics. Kinds of Digital Robots. Strengths and weaknesses of digital robotics. Introduction to BEAM robotics. Strengths and weaknesses of BEAM robots. Design. Performance of BEAM Robots
Introduction to Digital Robotics Programmed with If/Then statements, providing criteria and appropriate, pre-set behavior for each situation. Any defects in sensors used leads to malfunction. Examples: QRio (Sony), Asimo (Honda)
Kinds of Digital Robots Entertainment (example: Sony Aibo or QRio) Industrial (example: painting and welding arms) Special-Effects (example: Jurrasic Park Dinosaurs) Research-based (example: Mars Rover, Honda Humanoid)
Digital Robotics Strengths Precise, controllable movements. If/Then statements dictate exact behavior. Can repeat same task over and over. Weaknesses Can only deal with predicted events. Less scalable technology. Requires massive computing power for simple tasks. Usually not self-contained. (separate computer) Cannot function if damaged. Not suited to natural environment.
Introduction to BEAM Robotics What it means: Biology, Electronics, Aesthetics, Mechanics. Design philosophy: Simplest possible design to perform a specific task. (No unessential features). Uses no programming. Mimics organic life. Actions dictated by physical design. High survivability.
Different types of BEAM Robots. AUDIOTROPE-which reacts to the sound sources
RADIOTROPE-which reacts to the Radio frequency sources.
BEAM Robotics Strengths Adaptable behavior to fit situation. Energy efficient. Durable. Renewable power source. Adaptable to damage. Protects self. Can survive outside laboratory environment. Weaknesses Random behavior. Not as taskable. Inexact movements. Not suited to “factory” environment. Reflexive.
Design 4 limbs. 2 solar cells i.e 1 for use and one for backup. RF sensor i.e only to sense the enemy signals. Neural networks to avoid collision Wireless camera. A position locater to locate the robot.
ARITIFICIAL NEURAL NETWORKS(ANN) An artificial neural network is composed of many artificial neurons that are linked together according to a specific network architecture Neurons mimic the properties of biological neurons. Each neuron of an artificial network learn from the environment in which robot is left.
ANN-implementation try to create an learning environment which comprises of enemy TX, host TX and actual war field environment. make neural networks to learn in this environment and make motors(which drives the wheels of robot) to run according to knowledge gained from neurons. Now the robot is left in the war field to find enemy TX. Robot reach the enemy TX without any collision due to neural networks implementation.
LEARNING ENVIRONMENT Gaining Knowledge about Surrounding environment Robot-env 4 Robot-env 2 ENEMY TX HOST TX Robot-env 3 Robot-env 6 Robot-env 1 Robot-env 5 Learning process is completed
Robot specifications Low charge Solar cell 1 Solar cell 2 radiotrope Charging Navigation of robot using neural netw Position Locater
Enemy tx. RF SIGNALS Enemy signal detected radiotrope our tx. RF signals Missile launcher Position obtained
Performance Dependabilty of BEAM Robots Dependabilty of DIGITAL Robots Advancement in the field of BEAM Robotics
Conclusion The application of BEAM robots will be most effective for defence purpose. Navigation of robot is more accurate by using ANN. The end product is reliable and efficient.