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DIFFERENTIALLY CONSTRAINED MOTION PLANNING WITH STATE LATTICE MOTION PRIMITIVES (2012)
| Citations: | 5 - 0 self |
BibTeX
@MISC{Pivtoraiko12differentiallyconstrained,
author = {Mihail N. Pivtoraiko},
title = {DIFFERENTIALLY CONSTRAINED MOTION PLANNING WITH STATE LATTICE MOTION PRIMITIVES},
year = {2012}
}
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Abstract
Robot motion planning with differential constraints has received a great deal of attention in the last few decades, yet it still remains a challenging problem. Among a number of reasons, three stand out. First, the differential constraints that most physical robots exhibit render the coupling between the control and state spaces quite complicated. Second, it is commonly accepted that robots must be able to operate in environments that are partially or entirely unknown; classical motion planning techniques that assume known structure of the world frequently encounter difficulties when applied in this setting. Third, such robots are typically expected to operate with speed that is commensurate with that of humans. This poses stringent limitations on available runtime and often hard real-time requirements on the motion planner. The impressive advances in computing capacity in recent years have been unable, by themselves, to meet the computational challenge of this problem. New algorithmic approaches to tackle its difficulties continue to be developed to this day. The approach advocated in this thesis is based on encapsulating some of the complexity of satisfying the differential constraints in pre-computed controls that serve as motion primitives, elementary motions that are combined to form the solution trajectory for the system. The contribution of this work is in developing a general approach to constructing such motion primitives,
Keyphrases
differential constraint motion primitive robot motion planning challenging problem impressive advance last decade computational challenge pre-computed control hard real-time requirement classical motion planning technique solution trajectory general approach state space great deal physical robot exhibit available runtime encounter difficulty new algorithmic approach elementary motion recent year stringent limitation motion planner
