1 / 7

Innovative Tree-Based Planning Method for Motion Pathfinding

This research presents a novel 'Single Shot' tree-based planning technique that efficiently determines a motion plan connecting start and goal configurations without the need for preprocessing or a roadmap. This method can also be applied to scenarios where a specific goal is not defined but exploration is required from a given start state. Utilizing a bi-directional search, two trees are iteratively grown from both the start and the goal until they intersect, thereby solving the motion planning problem dynamically. The algorithm leverages methodologies from Rapidly Exploring Random Trees (RRT) to enhance efficiency.

alegria-martinez
Télécharger la présentation

Innovative Tree-Based Planning Method for Motion Pathfinding

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Tree-Based Planning Nancy M. Amato Parasol Lab,Texas A&M University

  2. ‘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

  3. 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

  4. 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]

  5. 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

  6. Rapidly Exploring Random Trees (RRT) [Kuffner & LaValle ICRA 1999]

  7. 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.

More Related