NDLI: Dynamically feasible, energy efficient motion planning for skid-steered vehicles

Content Provider	Springer Nature Link
Author	Gupta, Nikhil Ordonez, Camilo Collins, Emmanuel G.
Copyright Year	2016
Abstract	Recent research has developed experimentally verified dynamic models for skid-steered wheeled vehicles and used these results to derive a power model for this important class of all-terrain vehicles. As presented in this paper, based on the torque limitations of the vehicle motors, the dynamic model can be used to develop payload and terrain-dependent minimum turn radius constraints and the power model can be used to predict the energy consumption of a given trajectory. This paper uses these results along with sampling based model predictive optimization to develop an effective methodology for generating dynamically feasible, energy efficient trajectories for skid-steered autonomous ground vehicles (AGVs) and compares the resultant trajectories with those based on the standard distance optimal trajectories. The simulated and experimental results consider an AGV moving at a constant forward velocity on both wood and asphalt surfaces under various payloads. The results show that a small increase in the distance of a trajectory over the distance optimal trajectory can result in a dramatic savings in the AGV’s energy consumption. They also show that distance optimal planning can often produce trajectories that violate the motor torque constraints for skid-steered AGVs, which can result in poor navigation performance.
Starting Page	453
Ending Page	471
Page Count	19
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	41
Issue Number	2
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2016-03-02
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Skid-steered vehicles Dynamic modeling Power modeling Motion planning Energy efficient Robotics and Automation Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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