Abstract
The mobile robot is moved by receiving instructions through wireless communication, and the particle filter is used to simultaneous localization and mapping. Aiming at the problem of the degradation of particle filter weights and loss of particle diversity, which leads to the decrease of filter accuracy, this paper uses the plant cell swarm algorithm to optimize the particle filter. First of all, combining the characteristics of plant cells that affect the growth rate of cells when the auxin content changes due to light stimulation realizes the optimization of the particles after importance sampling, so that they are concentrated in the high‐likelihood area, and the problem of particle weight degradation is solved. Secondly, in the process of optimizing particle distribution, the auxin content of each particle is different, which makes the optimization effect on each particle different, so it effectively solves the problem of particle diversity loss. Finally, a simulation experiment is carried out. During the experiment, the robot moves by receiving control commands through wireless communication. The experimental results show that the algorithm effectively solves the problem of particle weight degradation and particle diversity loss and improves the filtering accuracy. The improved algorithm is verified in the simultaneous localization and mapping of the robot, which effectively improves the robot’s performance at the same time positioning accuracy. Compared with the classic algorithm, the robot positioning accuracy is increased by 49.2%. Moreover, the operational stability of the algorithm has also been improved after the improvement.
Highlights
The particle filter (PF) algorithm is a filtering method based on Monte Carlo and Bayesian estimation
In order to solve the problem of particle weight degradation, the resampling strategy is introduced in the classical particle filter algorithm, and the corresponding algorithm is called Rao-Blackwellized particle filters (RBPF); Rao-Blackwellized particle filter is applied to robotic simultaneous localization and mapping (SLAM) and is named FastSLAM1.0 algorithm [4]
When the particle number is 100, the classical PF algorithm, the gravitational field-based optimization PF algorithm (GFAPF) [8], and the improved algorithm proposed in this paper (PCSA-PF) are compared
Summary
The particle filter (PF) algorithm is a filtering method based on Monte Carlo and Bayesian estimation. The attraction and repulsion of the central dust, the particle set is distributed near the high-likelihood region, which alleviated the weight degradation and improved the diversity of particles [8] This improved algorithm has been successfully applied to SLAM and achieved a good positioning effect. The main steps of the classical PF algorithm are as follows: initialization particle set, importance sampling, importance weight calculation, resampling, and state estimation. This improvement makes the proposal distribution function contain historical information and observation information at the current moment It will effectively alleviate the problem of algorithm degradation and improve the filtering accuracy of the algorithm [13]. In order to solve the problem of particle weight degradation, the FastSLAM2.0 algorithm adopts a resampling strategy, which will cause the loss of particle diversity and reduce robot positioning accuracy. The key to improving the accuracy of robot positioning and mapping is to effectively solve the particle weight degradation and loss of particle diversity [14]
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