Research On Mixed Evidence Theory Based Multi-Robot Modeling

Recently,the importance of research on multi-robot system is more significant in mobile multi-robot area.With the development of hardware,for example control for single robot,improvement for sensing precision,many researchers focus on the technique of multi-robot cooperatively working.And many projects of multi-robot system have been successfully developed.The main challenge for multi-robot system is that the effective strategy of multi-robot cooperation should be elaborately designed,which enables multi-robot efficiently accomplish the task.In many multi-robot based applications,SLAM has great effect on the total performance of multi-robot system,which has raised more research attention.Firstly,this thesis describes the procedure of robot acquiring local environment information.And we introduce the process of extracting local environment information,which is scanned by distance laser sensor.The above procedures get ready for constructing accurate environment model.Then this thesis proposes clustering structure based multi-robot cooperative location algorithm.The main idea of the algorithm is as follows.In the clustering networks,the robots locate themselves through matching each robot and their neighbor robots.The approach given in this thesis reduces the time complexity of locating algorithm and improves the efficiency.Secondly,this thesis focuses on designing map building algorithm with low computation and communication overhead.To bring down the computation cost of map building algorithm,this thesis utilizes topology-grid mixed structure based environment description method.Then this thesis provides the model of topology map and introduces the detailed procedure for transforming the local environment map to topology map.Moreover,this thesis offers the concrete steps for building the topology environment model,and designs topology-grid mixed structure based map building algorithm.Finally,the method of multi-robot cooperatively building map needs to solve the information fusion among multiple robots,which can reflect the advantages of multi-robot system.In the process of cooperatively building map,robots exchange information and transmit data by wireless communication.According to the received information,the robot realizes map matching in order to construct accurate global map.To reduce the effect of information uncertainty in map building,this thesis researches on evidence theory and particle swarm optimization algorithm based parameter optimization,which is helpful in building global topology map.The optimized parameters make the multi-robot build global map more adaptively,which results in more real environment map.