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Abstract

As mobile robots venture into more difficult environments, more complex state-space paths are required to move safely and efficiently. The difference between mission success and failure can be determined by a mobile robot's capacity to effectively navigate such paths in the presence of disturbances. This paper describes a technique for mobile robot model predictive control that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved solution. The contribution, a receding horizon model-predictive control (RHMPC) technique, specifically addresses the problem of path following and obstacle avoidance through geometric singularities and discontinuities such as cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to relax parameterized control inputs initialized from a regional motion planner to navigate safely through the environment. Experimental results are presented for a six-wheeled skid-steered field robot in natural terrain.

Notes

Associated Project(s): UGCV PerceptOR Integrated

Text Reference

Thomas Howard, Colin Green, and Alonzo Kelly, "Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths," Proceedings of the 7th International Conferences on Field and Service Robotics, July, 2009.

BibTeX Reference

@inproceedings{Howard_2009_6514,
   author = "Thomas Howard and Colin Green and Alonzo Kelly",
   title = "Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths",
   booktitle = "Proceedings of the 7th International Conferences on Field and Service Robotics",
   month = "July",
   year = "2009",
}

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