The use of unicycle robot control strategies for skid-steer robots through the ICR kinematic mapping

Pentzer, Jesse; Brennan, Sean; Reichard, Karl

doi:10.1109/iros.2014.6943006

Cited by 18 publications

(16 citation statements)

References 8 publications

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“…Martinez et al [25,26] proposed an instantaneous center of rotation (ICR)-based tracked robot model. Real-time estimation of the ICR has shown to improve robot localization [27] and trajectory tracking [28]. However, the fact that even a small amount of longitudinal slip can result in very large ICR values when the robot is moving in a straight line render it unsuitable for path following controllers.…”

Section: Literature Reviewmentioning

confidence: 99%

Active Disturbance Rejection Control for Handling Slip in Tracked Vehicle Locomotion

Sebastian

Ben-Tzvi

2019

Journal of Mechanisms and Robotics

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This paper describes the use of an active disturbance rejection controller (ADRC) to estimate and compensate for the effect of slip in an online manner to improve the path tracking performance of autonomous ground vehicles (AGVs). AGVs with skid-steer locomotion mode are extensively used for robotic applications in the fields of agriculture, transportation, construction, warehouse maintenance, and mining. Majority of these applications such as performing reconnaissance and rescue operations in rough terrain or autonomous package delivery in urban scenarios, require the system to follow a path predetermined by a high-level planner or based on a predefined task. In the absence of effective slip estimation and compensation, the AGVs, especially tracked vehicles, can fail to follow the path as given out by the high-level planner. The proposed ADRC architecture uses a generic mathematical model that can account for the scaling and shift in the states of the system due to the effects of slip through augmented parameters. An extended Kalman filter (EKF) observer is used to estimate the varying slip parameters online. The estimated parameters are then used to compensate for the effects of slip at each iteration by modifying the control actions given by a low-level path tracking controller. The proposed approach is validated through experiments over flat and uneven terrain conditions including asphalt, vinyl flooring, artificial turf, grass, and gravel using a tracked skid-steer mobile robot. A detailed discussion on the results and directions for future research is also presented.

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Section: Literature Reviewmentioning

confidence: 99%

Active Disturbance Rejection Control for Handling Slip in Tracked Vehicle Locomotion

Sebastian

Ben-Tzvi

2019

Journal of Mechanisms and Robotics

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“…However, these approaches do not offer experimental evaluation at higher speeds. One solution to path following using skid-steered vehicles is proposed in [Pentzer et al, 2014b], where a unicycle control strategy is used to control a skid-steered robot with the help of a kinematic mapping. This mapping is based on the parameter estimation proposed in [Pentzer et al, 2014a].…”

Section: Introductionmentioning

confidence: 99%

High-speed path following control of skid-steered vehicles

Huskic

Buck

Herrb

et al. 2019

The International Journal of Robotics Research

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We present a robust control scheme for skid-steered vehicles that enables high-speed path following on challenging terrains. First, a kinematic model with experimentally identified parameters is constructed to describe the terrain-dependent motion of skid-steered vehicles. Using Lyapunov theory, a nonlinear control law is defined, guaranteeing the convergence of the vehicle to the path. To allow smooth and accurate motion at higher speeds, an additional linear velocity control scheme is proposed, which takes actuator saturation, path following error, and reachable curvatures into account. The combined solution is experimentally evaluated and compared against two state-of-the-art algorithms, by using two different robots on several different terrain types, at different speeds. A Robotnik Summit XL robot is tested on three different terrain types and two different paths at speeds up to [Formula: see text] m/s. A Segway RMP 440 robot is tested on three different terrain types and two different path types at speeds up to [Formula: see text] m/s.

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“…TC stands for terrain classification, BF stands for Bayesian filter, and KF stands for Kalman filter. 4 Complexity…”

Section: Terrain Adaptive Ikfmentioning

confidence: 99%

“…However, due to the presence of the large contacting patches between the tracks and the ground, unpredictable slip is inevitable, which makes it difficult to build a precise kinematics model [1]. Because most strategies that concern navigation, motion control, obstacle avoidance, and route planning are designed based on the kinematics model, a robot's performance can be significantly improved by the online identification of terrain-related slip parameters included in the kinematics model [2][3][4][5][6][7][8]. Therefore, this paper concentrates on designing a data fusion approach to acquire slip parameters in real time.…”

Section: Introductionmentioning

confidence: 99%

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

Wang

et al. 2018

Complexity

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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.

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The use of unicycle robot control strategies for skid-steer robots through the ICR kinematic mapping

Cited by 18 publications

References 8 publications

Active Disturbance Rejection Control for Handling Slip in Tracked Vehicle Locomotion

Active Disturbance Rejection Control for Handling Slip in Tracked Vehicle Locomotion

High-speed path following control of skid-steered vehicles

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

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