Research On Multi-Robot Map Building Simultaneous Localization Algorithm

Map building and self-localization not only is very important to accomplish autonomous navigation and other complex intelligent tasks, but also embodies perception ability and intelligence of robots. Mobile robot localization is an important topic in the intelligent robot domain. It includes sensory techniques and localization algorithms. An advantage of global localization is that the localization algorithm provides a means of estimating robot position under global uncertainty.This thesis focuses on the self-localization algorithm for mobile robots in unknown environment, and, mainly on the map building, the localization approaches and based on dynamic area for the multi-robots Simultaneous Localization and Mapping problem. The research topics include approaches of Occupancy Grids, data association, based on dynamic area for the multi-robots map building.And, the Monte Carlo localization and MCL approach were studied. We introduced an algorithm based on Sherwood to solve the multi-robot SLAM problem. The theoretical analysis and results of experiments show that the mapping of the dynamic area can greatly improve exploring efficiency and Sherwood algorithm can enhance the real-time of localization of each robot.The multi-robots collaborating under controlled by the same worksite was studied. And a Sherwood algorithm approach based on dynamic area for the multi-robots Simultaneous Localization and Mapping (SLAM) problem was proposed. It can calculate the location of the robots in the joint map by Sherwood algorithm. This task can be done with using a collection of the environment features of their own planar surface on which these robots are moving. The theoretical analysis and results of experiments showed that the mapping of the dynamic area can greatly improve exploring efficiency and Sherwood algorithm can enhance the real-time of localization of each robot.