Abstract
The existing literature proposes various (neuronal) architectures for object avoidance, which is one of the very fundamental tasks of autonomous, mobile robots. Due to certain hardware limitations, existing research resorts to prespecified sensor systems that remain fixed during all experiments, and modifications are done only in the controllers' software components. Only recent research (Lichtensteiger and Eggenberger, 1999) has tried to do the opposite, i.e., prespecifying a simple neural network and evolving the sensor distribution directly in hardware. Even though first experiments have been successful in evolving some solutions by means of evolutionary algorithms, they have also indicated that systematic comparisons between different evolutionary algorithms and codings schemes are required in order to optimize the evolutionary process. Since these comparisons cannot be done on the robot due to experimentation time, this paper reports the result of a set of extensive simulations.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
