Experimental analysis of models for trajectory generation on tracked vehicles

Fink, Jonathan; Stump, Ethan

doi:10.1109/iros.2014.6942824

Cited by 9 publications

(1 citation statement)

References 15 publications

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“…A recent comparison of some of these models can be found in Ref. [23]. One notable work in this direction includes a model predictive optimal control presented by Rajagopalan et al [24].…”

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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“…A recent comparison of some of these models can be found in Ref. [23]. One notable work in this direction includes a model predictive optimal control presented by Rajagopalan et al [24].…”

Section: Literature Reviewmentioning

confidence: 99%