[1]WU Yongjian,CHEN Yuedong,CHEN Mengyuan.Research on RBPF-SLAM algorithm based on quantum-behaved particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2018,13(5):829-835.[doi:10.11992/tis.201705006]
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Research on RBPF-SLAM algorithm based on quantum-behaved particle swarm optimization
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 5
Page number:
829-835
Column:
学术论文—智能系统
Public date:
2018-09-05
- Title:
- Research on RBPF-SLAM algorithm based on quantum-behaved particle swarm optimization
- Keywords:
- RBPF; simultaneous localization and map building; proposed distribution; quantum-behaved particle swarm optimization; crossover and mutation; mobile robot; ROS
- CLC:
- TP21;TP24
- DOI:
- 10.11992/tis.201705006
- Abstract:
- The traditional Rao-Blackwellized particle filter (RBPF) is associated with a low distribution accuracy as well as particle degeneracy and loss of diversity during resampling. To solve these problems, a combination of RBPF and simultaneous localization and mapping (RBPF-SLAM) algorithm based on quantum-behaved particle swarm optimization (QPSO) is proposed. A fusion of robot motion model and observation model is proposed as a hybrid distribution to improve accuracy. The QPSO algorithm updates the pose of particles in the resampling process according to the weight measurement of particle type, and an adaptive crossover and mutation operation is introduced to optimize and adjust the particle set to effectively prevent particle degradation and maintain particle diversity. To verify the effectiveness of the improved algorithm, a simulation experiment is performed on MATLAB, as well as a Voyager-Ⅱ mobile robot in a robot operating system (ROS). The results show that the proposed algorithm can accurately estimate the position and pose of the robot and a high precision map, and error and running time are also greatly reduced.
- References:
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Last Update:
2018-10-25