@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Mihail Pivtoraiko's publication pages at
@COMMENT http://www.seas.upenn.edu/~mihailp/pub/class_type.html
@TECHREPORT{pivtoraiko_knepper_kelly_tr07,
  author = {Mihail Pivtoraiko and Ross A. Knepper and Alonzo Kelly},
  title = {Optimal, Smooth, Nonholonomic Mobile Robot Motion Planning in State
	Lattices},
  institution = {Robotics Institute, Carnegie Mellon University},
  year = {2007},
  number = {CMU-RI-TR-07-15},
  abstract = {We present an approach to the problem of mobile robot
                  motion planning in arbitrary cost fields subject to
                  differential constraints. Given a model of vehicle
                  maneuverability, a trajectory generator solves the
                  two point boundary value problem of connecting two
                  points in state space with a feasible motion. We use
                  this capacity to compute a control set which
                  connects any state to its reachable neighbors in a
                  limited neighborhood.  Equivalence classes of paths
                  are used to implement a path sampling policy which
                  preserves expressiveness while eliminating
                  redundancy. The implicit repetition of the resulting
                  minimal control set throughout state space produces
                  a reachability graph that encodes all feasible
                  motions consistent with this sampling policy.  The
                  graph encodes only feasible motions by construction
                  and, by appropriate choice of state space dimension,
                  can permit full configuration space collision
                  detection while imposing heading and curvature
                  continuity constraints at nodes. Nonholonomic
                  constraints are satisfied by construction in the
                  trajectory generator. We also use the trajectory
                  generator to compute an ideal admissible heuristic
                  and significantly improve planning efficiency.
                  Comparisons to classical grid search and
                  nonholonomic motion planners show the planner
                  provides better plans or provides them faster or
                  both. Applications to planetary rovers and
                  terrestrial unmanned ground vehicles are
                  illustrated.},
  bib2html_pubtype = {Tech Reports},
  bib2html_rescat = {Kinodynamic Planning},
  owner = {mihail},
  timestamp = {2010.08.07}
}