Bio Inspired Neural Networking Among Multi-Robots

Powerful Essays
BIO INSPIRED NEURAL NETWORKING AMONG
MULTI-ROBOTS

CHAPTER 1
INTRODUCTION

Transportation is one of the most important economic activities of any country. Among the various forms of transport, road transport is one of the most popular means of transportation. Transportation has an element of danger attached to it in the form of vehicle crashes. Road crashes not only cause death and injury, but they also bring along an immeasurable amount of agony to the people involved. Efforts to improve traffic safety to date have concentrated on the occupant protection, which had improved the vehicle crash worthiness. The other important area where research is currently being done is collision avoidance. Technological innovations have given the traffic engineer an option of improving traffic safety by utilizing the available communication tools and sophisticated instruments. Using sensors and digital maps for increasing traffic safety is in its infancy. Systems are being developed to utilize the available state of the art facilities to reduce or possibly prevent the occurrence of crashes. Total prevention of crashes might not be possible for now, but the reduction of crashes could easily be achieved by using the collision avoidance systems.

1.1 NEED FOR COLLISION AVOIDANCE
The development of collision avoidance systems is motivated by their potential for increased vehicle safety. Half of the more than 1.5 million rear-end crashes that occurred in 1994 could have been prevented by collision avoidance systems .Collision avoidance systems can react to situations that humans cannot or do not, due to driver error. Therefore, they are able to reduce the severity of accidents. Figure 1 below indicates that about 45 percent of the crashes that occur are caused by human errors. Human errors that cause crashes include failure to keep in proper lane, failure to yield right of way, inattentive, failure to obey traffic control devices, operating vehicle in negligent manner, drowsy



References: pp. 571–583, Sep. 2007. focused on coordination,” IEEE Trans. Syst., Man, Cybern., Part B: Cybern., vol. 34, no. 5, pp. 2015–2028, Oct. 2004. [5] Z. Cao, M. Tan, L. Li, N. Gu, and S. Wang, “Cooperative hunting by distributed mobile robots based on local interaction,” IEEE Trans. Robot., vol. 22, no. 2, pp. 403–407, Apr. 2006. algorithms,” J. Intell. Robot. Syst.: Theory Appl., vol. 35, no. 1, pp. 111–122, Sep. 2002. Part B: Cybern., vol. 31, no. 3, pp. 302–318, Jun. 2001. 367–385, Jun. 2003.

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