Swarm Robotics Ppt Download Template
What is Swarm Robotics What are the Future Applications applications of collaborative behavior Swarm Robotics Scalability Synchronous Tasks Resilience to Environments Benefits What are the Current Developments Berman, S., Halasz, A., Kumar, V., & Pratt, S. Bio-inspired group behaviors for the deployment of a swarm of robots to multiple destinations. Informally published manuscript, General Robotics, Automation, Sensing and Perception Laboratory, University of Pennsylvania, Retrieved from Jevtic, A., & Andina, D. Swarm intelligence and its applications in swarm robotics. Conference on computational intelligence, man-machine systems and cybernetics, Tenerife, Spain.
Download Radix 64 Bits. Featured PowerPoint Templates and Themes. (widescreen) PowerPoint. Feathered PowerPoint. Mesh PowerPoint. Berlin PowerPoint. Depth PowerPoint. Get massive collection of Architecture and Buildings PowerPoint Template. Edit it and make it more attractive according to your topic and requirement. Introduction: To tackle complex real world problems, scientists have been looking into natural processes and creatures - both as model and metaphor - for years. Optimization is at the heart of many natural processes including Darwinian evolution, social group behavior and foraging strategies.
Retrieved from Kumar, V. (Performer) (2012). The Yuppie Handbook 1984 Camaro. Vijay kumar: Robots that fly and cooperate [Web].
Retrieved from McLurkin, J. (Performer) (2012). James McLurnkin on swarm robotics: 'why a thousand robots are better than one' [Web]. Retrieved from Morlok, R., & Gini, M. Dispersing robots in an unknown environment. Informally published manuscript, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, Retrieved from Nano quadrotors: Quadricopters with artificial swarm intelligence by grasp lab & kmel robotics [Web]. Retrieved from www.youtube.com/watch?v=UQzuL60V9ng Truszkowski, W., Hinchey, M., Rash, J., & Rouff, C.
Nasa’s swarm missions: The challenge of building autonomous software. IT Pro, 47-52.
Retrieved from Sources.
Implementing a Swarm Robotics Scheme with Radio and Optical Communications Abstract One of the interesting topics being explored in in the field of robotics is that of swarm intelligence. Swarm intelligence is based on the concept that, a number of simply embodied agents, with limited individual intelligence, can produce a complex emergent behavior through interaction between each other and their environment. Cinematic Strings 2 Rapidshare Premium more.
This is observable in nature in the case of ants or bees, hence the moniker “swarm.” Using the swarm concept, a two type robot regime has been designed for a quasi-two-dimensional environment, a group of nine robots of the first type, or watchers, can, through random exploration, locate a range limited radio beacon and communicate, through optical pulse emission and detection, the position of the target such that they can guide a robot of the second type, or the single seeker, to the target location. In order to test the effectiveness of this implementation scheme, a series of experiments has been devised, comparing the average time to target location between a single seeker alone within the enclosure to that of the relative swarm result; with another experiment comparing the time to target location for the seeker moving within the field of watchers, but for this case no communication takes place between the robots, only obstacle avoidance protocols will be implemented. Initial State: Random Orientation/Distribution Swarm Communication State Each watcher robot will randomly meander until it is within range of the radio beacon, or it receives an optical signal from one of the other watchers. The watcher within range of the beacon will stop and emit an optical pulse indicating that it is number 1, (within range of the beacon). Any other watchers that detect the emitted pulse of those near the beacon will then stop and emit a pulse indicating that they are number 2, and any that receive their pulse will stop and emit a pulse indicating that they are number 3, and so on. With this arrangement in place the seeker will follow the pulses from higher number to lower until it is within range of the target.
Recipient: Gregg Carpenter, Electrical Engineering Faculty Mentor: Dr. Jeff Frolik, School of Engineering Approach Hardware Implementation For implementing the control logic the PIC18F452 microcontroller has been chosen. Programming of this device is done using Microchip’s MPLAB IDE and the PICICD2 in-circuit programmer/debugger. Three motors will be used, two DC motors in a differential drive configuration for the movement of the robots, and a continuous rotation servo will be used for the “neck” controlling the sweeping head which is used for optical pulse location.Photo-resistors will be used to implement the optical sensors. Software The software for controlling the robots is programmed in PIC-C using the following program flow: Seeker Flow DiagramWatcher Flow Diagram Simulation Simulation Screen Capture One of the major motivating factors behind performing a parallel simulation is the desire to model a large scale swarm interaction. Through comparison of the results of the simulation model with those observed in the physical test environment the accuracy of the simulation model can be determined and adjusted to more realistically describe the swarm interaction. Once a suitably accurate simulation has been developed this model can be used to predict the behavior of a much larger swarm environment involving hundreds or thousands of robots in order to explore the types of behavior that emerge from this interaction.