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。
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.
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