Research On Simulation Technology Of Multi-intelligent Vehicles Cooperation Formation Based On The Combination Of The Location

In today's intelligent vehicles research field, Multi-intelligent vehicles through the individual of mutual cooperation can complete complex tasks which a single intelligence vehicle can't complete, multi-intelligent vehicles formation system is one of the important research direction many intelligent vehicle technology. In the implementation of the process of some complex tasks, such as security guards, patrol, searches, secures etc, to keep a certain formation have the important meaning. Therefore, the study of how to control formation according to the goal, forming a designated formation for intelligent vehicles has important theoretical significance and practical value. With the location technology is the basis and the key technology for formation. Global Positioning System (GPS) has good long-term error precision, but short-term error is bigger, Dead Reckoning (DR) System will have good short-term precision, but long poor precision. On-board GPS-DR combination positioning technology can give some high precision and high frequency and high reliability of the location data through the data fusion. But because of the limitations of using GPS, this paper introduced visual data into the GPS-DR combination positioning technology and developed a new combination Localization method. The basic idea is to use visual sensor to perceive environment to create an environment map, establish obstacle avoidance path planning and complete GPS/DR positioning, use the environment map to correct vehicle pose estimation error and improve the localization precision; create a higher precision environment map according to the reliable vehicle pose.This paper provided a Iterated Central Difference Kalman Filter(ICDF) to compute the proposal distribution in Rao-Blackwellized Particle filter instead of the Extended Kalman Filter and fusion with new observation to obtain the Posteriori Probability, Estimate the position of the Vehicle and update the features of the environment by ICDKF. This algorithm decreases Computational-complexity, improves the Estimation of Performance and the stability of Iterated algorithm without decrease Accuracy. Simulation results are used to validate the effectiveness of the proposed algorithm.Because of the big cost, slower negotiation procession, lower adaption to the dynamic changed environment and other shortcomings for traditional contract nets agreement which applied in the fields of dynamic formation multi-tasking assign for Multi-intelligent vehicles, This paper integrated the Agent's character, confidence level and acquaintances of degrees, used the CBR technology to limit bidder, used the virtual Leader as the point of the target to design formation of this vehicles, and optimum allocated these target point to every formation vehicles through the Hungary Algorithm and the social potential field works for a MotorScheme behavior, designed multi-intelligent vehicles cooperation formation control method through the auction theory under the dynamic environment, realized some different formation for multi-intelligent vehicles through some proper strategy of cooperation and coordination, and designed a multi-agent simulation system platform for testing and analysising some strategy of cooperation formation.The simulation results show that the proposed algorithm is feasible and effective.