The Study Of WSNs And Mobile Robot Cooperative Positioning Based On Improved CKF

Along with the continual advancement of Wireless Sensor Networks(WSNs)and Mobile Robots(MR),nodes that constitute WSNs are functioning more and more powerfully,in the meanwhile,mobile robots in varied fields are getting widely applied for their diversified usages.Distributed nature,mutual communication frequency and sound sensing capacities shared between them render their combination to be a research highlight.When they are connected to make new WSNs-MR system,respective advantages remain while shortcomings and drawbacks get overcomed,which elevate its overall performance and simplifies initial problems on them.This thesis studied the location of WSNs-MR under the background of their combination.Firstly,simulation research on the positioning of WSNs' nodes in hybrid system was conducted.We created a MR-aided node positioning model cooperated by robot-node and node-node pattern,taking calculation advantages of MR and distributed nature of WSNs into consideration.This model involves modified CKF algorithm that was based on Gaussian segmentation and weighted strategy and performed predicting amendments to estimated position of nodes so that a highly-efficient and precise positioning of nodes could be realized.Secondly,we did some research on the positioning of MR under WSNs.WSNs' nodes could observe MR while its sensors could observe the environment's features,through which we built observation distance set and coordination set;then multi-constrained inequalities sets were employed to preliminarily estimate the measurements before the measurements were error-optimised with the aid of threshold discriminant and selective Gaussian segmentation Cubature Kalman filter(CKF)algorithm for an improved positioning.As the simulation result showed,this algorithm could yield better precision and level off more quickly.At last,the hybrid-systematic cooperative positioning model combining WSNs and MR was established,so was its mathematical expression.We also aggregated date of sensing information during the process to enhance the system in a whole and strengthen the precision of positioning.As we can tell from the simulation result,modified CKF algorithm based on threshold discriminant and selective Gaussian segmentation,after being integrated with WSNs-MR hybrid-system's assistant positioning and cooperative positioning,presents increased precision compared with that of conventional MR or WSNs.It alleviates bad influence from non-linear errors and anomaly errors and realize rapid balance which prove it to be effective.In a word,it sheds a light on why WSNs and MR should be combined to implement positioning and what it brings;and how this cooperation provides possibility to dynamic effective positioning under poor conditions.