Abstract An important envisaged application of legged robots is the exploration and mapping of extreme environments with an unknown terrain. Corin is a hexapod designed at the University of Manchester, which is able to perform motions using footholds on surfaces perpendicular to the ground plane. This allows it to be able to navigate through confined and narrow spaces. The hexapod requires an accurate estimate of its pose in order to be able to perform these motions. Current state-of-the-art state estimators for legged robots that solely use proprioceptive sensors, fuse inertial and leg kinematic measurements through an extended Kalman filter (EKF). This paper describes the implementation and validation of a state estimator on the Corin hexapod whilst performing motions using surface perpendicular to the ground plane. Another novelty of the work is the analysis of the algorithm sensitivity to the filter parameters and motion variables, and the tuning of the EKF using particle swarm optimisation (PSO). The results show that the average error achieved was below 6% for both position and orientation estimates.

中文翻译：

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六足状态估计器的调谐和灵敏度分析

摘要 腿式机器人的一个重要应用是未知地形的极端环境的探索和测绘。Corin 是曼彻斯特大学设计的六足动物，它能够使用垂直于地平面的表面上的立足点来执行运动。这使它能够在狭窄和狭窄的空间中航行。六足动物需要准确估计其姿态才能执行这些运动。当前最先进的足式机器人状态估计器仅使用本体感受传感器，通过扩展卡尔曼滤波器 (EKF) 融合惯性和腿部运动测量。本文描述了在使用垂直于地平面的表面执行运动的同时，在 Corin 六足位移台上的状态估计器的实现和验证。这项工作的另一个新颖之处是分析算法对滤波器参数和运动变量的敏感性，以及使用粒子群优化 (PSO) 调整 EKF。结果表明，位置和方向估计的平均误差均低于 6%。