| 巫宇航,王强,肖瑶,周海婷,吴琳琳,毛炜.燃气管道巡检四足机器人的改进沙猫群优化SLAM算法研究[J].电子测量与仪器学报,2024,38(10):128-136 |
| 燃气管道巡检四足机器人的改进沙猫群优化SLAM算法研究 |
| Study on improved sand cat swarm optimized SLAM algorithm forgas pipeline inspection quadruped robot |
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| DOI： |
| 中文关键词: 燃气巡检 沙猫群算法 地图构建 四足机器人 FastSLAM算法 |
| 英文关键词:gas inspection sand cat swarm optimization map building quadruped robot FastSLAM algorithm |
| 基金项目:浙江省‘尖兵’‘领雁’研发攻关计划（2022C03179）项目资助 |
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| Author | Institution |
| Wu Yuhang | College of Metrology Measurement and Instrument，China Jiliang University, Hangzhou 310018,China |
| Wang Qiang | College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou 310018, China |
| Xiao Yao | College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou 310018, China |
| Zhou Haiting | College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou 310018, China |
| Wu Linlin | College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou 310018, China |
| Mao Wei | Lanxi Xinao Gas Co. Ltd, Jinhua 321100, China |
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| 中文摘要: |
| 为解决燃气管道巡检四足机器人的地图构建问题，提出一种改进沙猫群算法优化的ISCSO-FastSLAM算法。首先，引入柯西变异策略提高沙猫群算法跳出局部最优的能力，加快收敛速度，并加入自适应遗传参数增强沙猫群算法的稳定性。再通过改进沙猫群算法输出的位置预测最优解来更新FastSLAM算法的预测粒子集，从而提高估计精度。同时利用低权重粒子优化策略代替粒子滤波中原来的重采样步骤，来保证粒子的多样性。然后搭建不同的仿真环境，将多种算法进行仿真对比，仿真结果表明：在20 m×20 m的仿真环境下，ISCSO-FastSLAM算法相比WOA-FastSLAM算法对地图的构建更为准确，对机器人位置和环境路标的估计误差分别减小了17.1%和23.3%。最后，利用四足机器人在60 m×100 m大小的居民区进行建图实验，实验结果表明：相比FastSLAM算法和WOA-FastSLAM算法，ISCSO-FastSLAM算法能够构建更准确的居民区巡检地图，对阀门井、调压箱等巡检关键位置的估计误差分别减小了16.2%和6.0%。 |
| 英文摘要: |
| To solve the map construction problem of the quadruped robot for natural gas pipeline inspection, an ISCSO-FastSLAM algorithm optimized by the improved sand cat swarm algorithm is proposed. Firstly, the Cauchy variation strategy is introduced to improve the ability of the sand cat swarm algorithm to jump out of the local optimum and accelerate the convergence speed, and the adaptive genetic parameters are added to improve the stability of the sand cat swarm algorithm. Then, the predicted particle set of the FastSLAM algorithm is updated by improving the optimal solution of the position prediction output of the sand cat swarm algorithm to improve the estimation accuracy. Meanwhile, the low weight particle optimization strategy is used to replace the original resampling step in particle filtering to ensure the diversity of particles. Then, different simulation environments are constructed to compare the different algorithms, and the simulation results show that the ISCSO-FastSLAM algorithm constructs the map more accurately than the WOA-FastSLAM algorithm, and the estimation errors of the robot position and the environmental signposts are reduced by 17.1% and 23.3%, respectively, under the simulation environment of 20 m×20 m. Finally, the quadruped robot is used to conduct map construction experiments in a residential area of 60 m×100 m, and the experimental results show that, compared with the FastSLAM algorithm and the WOA-FastSLAM algorithm, the ISCSO-FastSLAM algorithm is able to construct a more accurate map of the residential area inspection, and the estimation errors of the key inspection locations such as valve wells and regulator boxes are reduced by 16.2% and 6.0%, respectively. |
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