70 likes | 169 Views
Tree-Based Planning. Nancy M. Amato Parasol Lab,Texas A&M University. ‘Single Shot’ Planning. Given Start and Goal configurations, determine a motion plan connecting them without preprocessing (don’t build roadmap). Goal. Start.
E N D
Tree-Based Planning Nancy M. Amato Parasol Lab,Texas A&M University
‘Single Shot’ Planning Given Start and Goal configurations, determine a motion plan connecting them without preprocessing (don’t build roadmap) Goal Start • Also, can be applied when do not have specific goal, but want to find space reachable from start
G2 G3 S2 G1 S1 G4 S3 Bi-Directional Search: Iteratively grow trees from start and goal • Build two trees: one from start and one from goal • partial progress saved & added to evolving trees • Original query solved when start & goal trees meet G0 Obstacle3 Obstacle1 Obstacle2 S0
EXPLORE random walk terminus new Landmark SEARCH random walk terminus Ariadne’s Clew Algorithm [Bessiere et al IROS 1993] Goal Start [Bessiere et al, IROS 1993]
xnear xrand Random configuration Configurations around closest to random in tree New node added to the RRT tree Rapidly Exploring Random Trees (RRT) [Kuffner & LaValle ICRA 1999] Goal Start Nodes in current RRT-VAR tree
Rapidly Exploring Random Trees (RRT) [Kuffner & LaValle ICRA 1999]
xinit RRT approaches GENERATE_RRT(xinit, K, t) • T.init(xinit); • For k = 1 to K do • xrand RANDOM_STATE(); • xnear NEAREST_NEIGHBOR(xrand, T); • u SELECT_INTPUT(xrand, xnear); • xnew NEW_STATE(xnear, u, t); • T.add_vertex(xnew); • T.add_edge(xnear, xnew, u); • Return T; xrand xnew xnear The result is a tree rooted at xinit: LaValle, 1998; LaValle, Kuffner, 1999, 2000; Frazzoli, Dahleh, Feron, 2000; Toussaint, Basar, Bullo, 2000; Vallejo, Jones, Amato, 2000; Strady, Laumond, 2000; Mayeux, Simeon, 2000; Karatas, Bullo, 2001; Li, Chang, 2001; Kuffner, Nishiwaki, Kagami, Inaba, Inoue, 2000, 2001; Williams, Kim, Hofbaur, How, Kennell, Loy, Ragno, Stedl, Walcott, 2001; Carpin, Pagello, 2002.