One Moving Target Location Method Of Robot Networks Based On Distributed Particle Filter

In recent years,the rapid integration of emerging technologies represented by the Internet,big data,artificial intelligence and manufacturing has promoted the rapid development of industrial intelligent manufacturing and related industries such as robots.The development of the robot industry in the future must occupy a pivotal position in the national intelligent manufacturing development strategy.Faced with such a broad industry development prospect,robot-related technologies must be constantly adjusted and breakthroughs in order to have better performance,higher intelligence,and application to higher-end industries.Throughout the existing robot related research,the positioning problem is particularly critical as the basis and prerequisite for the development of various technologies.Generally speaking,the physical model we have created is inevitably affected by nonlinearity,non-Gaussian noise and interference in the real environment.As an important research content in the field of data processing such as nonlinear and non-Gaussian,the particle filter algorithm not only has the advantage of not limiting the noise model,but also has achieved certain results in object tracking and positioning.Position the moving target to solve the model noise problem.First of all,although the particle filter algorithm can also achieve the effect of positioning the target,it will cause large errors due to particle degradation and other reasons,so it is proposed to use the traceless transformation theory to improve it to obtain the traceless particle filter algorithm in order to obtain a better Estimate accuracy.Finally,through simulations of several filtering algorithms,it is concluded that the improved filtering algorithm does have better filtering accuracy and accuracy.The development of multi-agent systems has promoted the research of robot networks and distributed systems to a certain extent.Since the filtering algorithm is only applied to the measurement data of a single robot,the final result may face problems such as low accuracy or unrepresentative estimation results.Therefore,the concept of a robot network is proposed,and the established directed network communication topology is used for information exchange and transmission to Receive filtered estimation data results from different robots.In order to make the final output of the system unique and stable,a finite time average consistency algorithm is proposed to converge to a stable value within a limited time range.That is,based on the distributed filtering algorithm and the idea of the average consistency algorithm,the collected data information of the moving target is fused to obtain the overall state estimation of the robot network to the position of the moving target.Experimental results show that this method can improve the information fusion and estimation ability of the robot network to a certain extent,not only can obtain accurate estimates of the position of the moving target,but also has good tracking accuracy and real-time performance,and can complete the positioning of the moving target.