基于FastSLAM的绳系机器人同时定位与地图构建算法

doi:10.7527/S1000-6893.2020.23893

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基于FastSLAM的绳系机器人同时定位与地图构建算法

王小涛¹, 张家友¹, 王邢波², 韩亮亮³

1. 南京航空航天大学航天学院, 南京 210016;
2. 南京邮电大学自动化学院, 南京 210023;
3. 上海市空间飞行器机构重点实验室, 上海 201108

收稿日期:2020-02-25 修回日期:2020-03-21 发布日期:2020-05-21
通讯作者: 王邢波 E-mail:sinbowang@163.com
基金资助:
载人航天预先研究项目（030601）；民用航天技术预先研究项目（D030103）

Simultaneous localization and mapping algorithm based on FastSLAM framework for tethered robots

WANG Xiaotao¹, ZHANG Jiayou¹, WANG Xingbo², HAN Liangliang³

1. Academy of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
3. Shanghai Key Laboratory of Spacecraft Mechanism, Shanghai 201108, China

Received:2020-02-25 Revised:2020-03-21 Published:2020-05-21
Supported by:
Advanced Research Project on Manned Aerospace Technology (030601);Advanced Research Project on Civil Aerospace Technology (D030103)

摘要/Abstract

摘要： 绳系式移动机器人可用于极端地形的探测，如陡峭斜坡、松软土壤、高耸悬崖、沟壑等。在运动过程中移动机器人的绳索不可避免地与障碍物接触甚至缠绕。由于绳索与障碍物之间的接触点不相互独立以及机器人模型的非线性特性，经典的FastSLAM框架不适用于绳索机器人的同时定位和地图创建（SLAM）问题。提出基于改进FastSLAM框架的绳系机器人SLAM算法。在该框架中，分别利用无迹滤波和粒子滤波解决接触点位置估计和机器人位姿估计问题，并利用非线性观测模型的无迹变换来简化粒子权重更新。仿真结果表明，该算法可有效地估计接触点位置，同时提高机器人位姿估计性能。

关键词: 移动机器人, 极端地形, 同时定位和地图创建(SLAM), 粒子滤波, 无迹变换

Abstract: Tethered mobile robots can be used to explore extreme terrains such as steep slopes, cliffs, and gullies. During the exploration process, the tethers of the mobile robots will inevitably contact or even wrap around the obstacles. The classic FastSLAM framework does not apply to the Simultaneous Localization and Mapping (SLAM) of tethered robots due to the dependence of the contact points between the obstacles and the robots, and the nonlinearity of the robot model. In this paper, a modified FastSLAM-based algorithm is proposed to solve the SLAM problem of the tethered robot, where an unscented filter and a particle filter are adopted to estimate the positions of the contact points and the pose of the tethered robot, respectively. An unscented transformation on the nonlinear observation model is utilized to simplify the updating of the particle weights. Simulation results show that the proposed approach can effectively localize the contact points and improve the estimation performance of the robot pose.

Key words: mobile robots, extreme terrains, Simultaneous Localization and Mapping (SLAM), particle filters, unscented transformation

中图分类号:

V249.12

王小涛, 张家友, 王邢波, 韩亮亮. 基于FastSLAM的绳系机器人同时定位与地图构建算法[J]. 航空学报, 2021, 42(1): 523893-523893.

WANG Xiaotao, ZHANG Jiayou, WANG Xingbo, HAN Liangliang. Simultaneous localization and mapping algorithm based on FastSLAM framework for tethered robots[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(1): 523893-523893.

参考文献

[1] ABAD-MANTEROLA P. Axel rover tethered dynamics and motion planning on extreme planetary terrain[D]. Pasadena:California Institute of Technology, 2012.
[2] 胡明, 邓宗全, 高海波, 等. 摇臂-转向架式月球探测车越障通过性分析[J]. 上海交通大学学报, 2005, 39(6):928-932. HU M, DENG Z Q, GAO H B, et al. Analysis of climbing obstacle trafficability on the six-wheeled Rocker-Bogie lunar rover[J]. Journal of Shanghai Jiao Tong University, 2005, 39(6):928-932(in Chinese).
[3] WETTERGREEN D, THORPE C, WHITTAKER R. Exploring Mount Erebus by walking robot[J]. Robotics and Autonomous Systems, 1993, 11(3-4):171-185.
[4] BARES J, WETTERNGREEN D. Dante Ⅱ:Technical description, results, and lessons learned[J]. The International Journal of Robotics Research, 1999, 18(7):621-649.
[5] PIRJANIAN P, LEGER C, MUMM E, et al. Distributed control for a modular, reconfigurable cliff robot[C]//IEEE International Conference on Robotics and Automation. Piscataway:IEEE Press, 2002:4083-4088.
[6] HUNTSBERGER T, STROUPE A, AGHAZARIAN H, et al. TRESSA:Teamed robots for exploration and science on steep areas[J]. Journal of Field Robotics, 2010, 24(11-12):1015-1031.
[7] NESNAS I A D, ABAD-MANTEROLA P, EDLUND J A, et al. Axel mobility platform for steep terrain excursions and sampling on planetary surfaces[C]//IEEE Aerospace Conference. Piscataway:IEEE Press, 2008:1-11.
[8] ABAD-MANTEROLA P, EDLUND J, BURDICK J, et al. Axel:A minimalist tethered rover for exploration of extreme planetary terrains[J]. IEEE Robotics and Automation Magazine, 2009, 16(4):44-52.
[9] MCMANUS C, FURGALE P, STENNING B, et al. Lighting-invariant visual teach and repeat using appearance-based lidar[J]. Journal of Field Robotics, 2013, 30(2):254-287.
[10] STENNING B, MCMANUS C, BARFOOT T. Planning using a network of reusable paths:A physical embodiment of a rapidly exploring random tree[J]. Journal of Field Robotics, 2013, 30(6):916-950.
[11] STOETER S A, PAPANIKOLOPOULOS N. Kinematic motion model for jumping scout robots[J]. IEEE Transactions on Robotics, 2006, 22(2):398-403.
[12] KUMAR T R V A, RICHARDSON R C. Tether monitoring techniques for environment monitoring, tether following and localization of autonomous mobile robots[C]//2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE Press, 2008:2109-2114.
[13] KUMAR T R V A, RICHARDSON R C. Entanglement detection of a swarm of tethered robots in search and rescue applications[C]//Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, 2007:143-148.
[14] RAJAN V A K T, NAGENDRAN A, DEHGHANI-SANIJ A, et al. Tether monitoring for entanglement detection, disentanglement and localisation of autonomous robots[J]. Robotica, 2014, 34(3):527-548.
[15] MURTRA A C, MIRATS J M. IMU and cable encoder data fusion for in-pipe mobile robot localization[C]//IEEE Conference on Technologies for Practical Robot Applications (TePRA). Piscataway:IEEE Press, 2013:1-6.
[16] MCGAREY P, MACTAVISH K, POMERLEAU F, et al. The line leading the blind:Towards nonvisual localization and mapping for tethered mobile robots[C]//IEEE International Conference on Robotics and Automation. Piscataway:IEEE Press, 2016:4799-4806.
[17] THRUN S, FOX D, BURGARD W, et al. Robust Monte Carlo localization for mobile robots[J]. Artificial Intelligence, 2001, 128(1-2):99-141.
[18] DOUCET A, FREITAS J, GORDON N. Sequential Monte Carlo methods in practice[M]. New York:Springer-Verlag, 2001:499-515.
[19] MURPHY K. Bayesian map learning in dynamic environments[C]//The Conference and Workshop on Neural Information Processing Systems, 1999:1015-1021.
[20] MONTEMERLO M, THRUN S, KOLLER D, et al. FastSLAM:A factored solution to the simultaneous localization and mapping problem[C]//Proceedings of the AAAI National Conference on Artificial Intelligence, 2002:593-598.
[21] MCGAREY P, MACTAVISH K, POMERLEAU F, et al. TSLAM:Tethered simultaneous localization and mapping for mobile robots[J]. The International Journal of Robotics Research, 2017, 36(12):1363-1386.
[22] DURRANT-WHYTE H, BAILEY T. Simultaneous localization and mapping:Part I[J]. IEEE Robotics and Automation Magazine, 2006, 13(2):99-110.
[23] TRIGGS B, MCLAUCHLAN P F, HARTLEY R I, et al. Bundle adjustment-A modern synthesis[C]//International Workshop on Vision Algorithms, 1999:298-372.
[24] GRISETTI G, STACHNISS C, BURGARD W. Improved techniques for grid mapping with Rao-Blackwellized particle filters[J]. IEEE Transactions on Robotics, 2007, 23(1):34-46.

E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于FastSLAM的绳系机器人同时定位与地图构建算法

Simultaneous localization and mapping algorithm based on FastSLAM framework for tethered robots

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