Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Abstract

As a type of skid-steering mobile robot, the tracked robot suffers from inevitable slippage, which results in an imprecise kinematics model and a degradation of performance during navigation. Compared with the traditional robot, the kinematics model is able to reflect the influences of slippage through the introduction of instantaneous centres of rotation (ICRs). However, ICRs cannot be measured directly and are time-varying with terrain variation, and thus, here, we aim to develop an online estimation method to acquire the ICRs of a robot by means of data fusion technologies. First, an innovation-based extended Kalman filter (IEKF) is employed to fuse the readings from two incremental encoders and a GPS-compass integrated sensor, to provide a real-time ICR estimation. Second, a decision tree-based learning system is used to classify the terrains that the robot traverses, according to the vibration signals gathered by an accelerometer. The results of this terrain classification are improved via a Bayesian filter, by utilizing temporal correlation in the terrain time series. Third, the performances of the ICR estimation and terrain classification are mutually promoted. On one hand, terrain variation is detected with the aid of the terrain classification, and therefore, the process noise variance of IEKF can be automatically adjusted. Hence, the results of ICR estimation are smooth if the terrain does not change and converge rapidly upon terrain variation. On the other hand, the sudden changes in innovation are used to adjust the state transition probability during the recursive calculation of the Bayesian filter, thus increasing the accuracy of the terrain classification. A real-world experiment was undertaken on a tracked robot to validate the effectiveness of the proposed method. It is also demonstrated that the terrain adaptive odometry outperforms the traditional approach with the knowledge of ICRs.

Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification

“…This is because CB is relatively rigid and irregular, and CB-induced vibration is distinguishable compared with other terrain types, which is also demonstrated in Figure 4. (2) The data of AR are relatively clustered and barely intersect with those of NG, AG, SB, PT, both in the time domain and frequency domain, so AR could be recognised with the second accuracy. (3) The data of terrains other than CB and AR may intersect with others more or less, so there may exist confusion in the classification of the four terrains.…”

“…The existing literature has proposed many time-domain features and achieved an acceptable classification accuracy, but only a portion of them contribute primarily to the classification performance [24]. In this paper, the time-domain feature vector x = (x (1) , x (2) , • • • , x (10) ) is shown as follows:…”

“…Achieving autonomous motion of a mobile robot is one of the most challenging problems in robotics, and the key to its success consists of the following four parts: environmental perception, pose estimation, motion control and route planning [1]. The implementation of pose estimation, motion control and route planning often requires us to introduce environmental information to some extent, so accurate environmental perception is of great importance [2]. The environmental humps (e.g., walls) and sinks (e.g., rivers) that robots cannot traverse are referred to as geometric hazards, which have been investigated extensively [3].…”

Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

“…In [24], a hybrid scheme of EKF and KF was proposed to estimate the position and velocity of a 6-wheel skid-steered vehicle while only the longitudinal wheel slip was modeled. Recently, Wang et al [25] introduced an innovation-based EKF combined with Bayesian filter for robot navigating on various terrains. A similar approach was introduced in [26], the process noise covariance was adjusted aided by terrain vision.…”

Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

“…Although a large body of terrain classification methods based on VTC have been investigated, most of them are achieved by supervised learning without considering the unlabeled upcoming vibration data [ 25 , 30 , 31 , 32 , 33 , 34 ]. In fact, we cannot guarantee a sufficient sampling of training dataset, so it is nature to resort to the semi-supervised or unsupervised machine learning tools for VTC.…”