Research On Relocation Of Mobile Robot Based On Energy Map And Adaptive Particle Filter

Localization is one of the most fundamental and critical issues in the research of mobile robots.The result directly affects the success of the robot's expected tasks.The achievement of on accurate,robust and real-time positioning is the prerequisite for the application.Although the robot positioning problem in the common scene has been basically solved.There are still some problems in positioning in some special situations.For example,when a robot is maliciously kidnapped or subjected to an accidental impact,the posture of the robot usually changes suddenly,which leads to the failure of the positioning,and finally the robot cannot complete the intended task.Therefore,when the above situation occurs in the robot,how to quickly restore its positioning capability is an urgent problem to be solved.It is Relocation that recover robot localization usually called.This topic is related to the research of mobile robot relocation methods.Based on the current research on the relocation of 2D LIDAR and particle filter,in which the existing particle sampling efficiency is low,the paper proposes the improvement of relocation methods based on multi-resolution energy map,adaptive particle filter and fast particle set initial sampling,which optimizes the particle sampling process and accelerates the convergence efficiency of the particle set.The main work of this paper is as follows:1)Investigate and analyze the domestic and international research status in the relevant fields of this subject,and study the theory of robot probabilistic positioning to provide theoretical basis for the effective development of this topic.2)Propose the concept of multi-resolution energy map,and implement a relocation method MEM-SAMCL based on this.The method constructs a multi-resolution twodimensional energy map offline,and uses a backtracking search algorithm to find a region SER similar to the current environment;then,according to the change of the particle set average likelihood,it is judged whether the robot is kidnapped;if the robot is kidnapped,it is helpful to obtain a global particle set in the SER Sampling to discover the new pose of the robot;while maintain a local particle set for tracking positioning.Finally,the method was verified and analyzed by relevant contrast experiments.3)Based on the similar energy region SER,an improved particle swath initial sampling technique is proposed,and an adaptive particle filter relocation method KLD-MEM-SAMCL is realized by fusion with particle set scale dynamic adjustment technology.It can dynamically adjust the number of particles over time;the initial pose estimation can be performed without the moving robot,and the initial sampling of the particle set is performed around the estimated pose.Finally,the method was verified and analyzed by relevant contrast experiments.4)Briefly introduce the software and hardware platform used in the relocation system of this subject;apply the KLD-MEM-SAMCL relocation system to the robot navigation task,and further verify its positioning capability through experiments.