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This document delves into cutting-edge motion algorithms developed by Jean-Claude Latombe from Stanford University, focusing on the planning, simulation, and analysis of motion for physical objects. Latombe's extensive research spans areas like robotics, artificial intelligence, and the geometric complexities involved in motion planning. Key topics include Probabilistic Roadmaps, collision checking techniques, and applications in biological modeling and surgical simulation. This work underlines the importance of effective pathfinding in both simulated and real-world environments, fostering advancements in various technological fields.
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Motion Algorithms:Planning, Simulating, Analyzing Motion of Physical Objects Jean-Claude Latombe Computer Science Department Stanford University
Pernes-les-Fontaines About Myself • Born a long time ago in South-East of France
About Myself • Born a long time ago in South-East of France • Studied in Grenoble(Eng. EE, MS EE, PhD CS 1977) • CS Professor, Grenoble (1980-84) • CEO, ITMI (1984-87) • Stanford (1987-…)
Research Interests • 1980-84: Artificial Intelligence, Computer Vision, Robotics • 1987-92: Robot Motion Planning • 1993-98: Motion Planning • 1998-…: Motion Algorithms
Fundamental Question Are two given points connected by a path?
How Do You Get There? • Problems: • Geometric complexity • Space dimensionality
Assembly planning Target finding New Problems
Space Robots robot obstacles air thrusters gas tank air bearing
Modular Reconfigurable Robots Casal and Yim, 1999 Xerox, Parc
Humanoid Robot [Kuffner and Inoue, 2000] (U. Tokyo) Stability constraints
Study of Molecular Motion Ligand binding Protein folding
Basic Tool: Configuration Space Approximate the free space by random sampling Probabilistic Roadmaps [Lozano-Perez, 80]
Probabilistic Roadmap (PRM) free space
local path milestone mg mb Probabilistic Roadmap (PRM) free space
[Quinlan, 94; Gottschalk, Lin, Manocha, 96] First Assumption of PRM Planning Collision tests can be done efficiently. Several thousand collision checks per second for 2 objects of 500,000 triangles each on a 1-GHz PC
Exact Collision Checking of Path Segments • Idea: Use distance computation in workspace rather than pure collision checking D= 2Lx|dq1|+L|dq2| 3Lxmax{|dq1|,|dq2|} If D d then no collision d q2 q1
Second Assumption of PRM Planning A relatively small number of milestones and local paths are sufficient to capture the connectivity of the free space.
Probabilistic Completeness In an expansive space, the probability that a PRM planner fails to find a path when one exists goes to 0 exponentially in the number of milestones (~ running time).
vi Pij vj Application to Biology Markov chain + first-step analysis ensemble properties
Current Projects • Robot motion planningFunding: General Motors, ABBCollaborator: Prinz (ME), Rock (AA) • Study of molecular motions (folding, binding)Funding: NSF-ITR (with Duke and UNC), BioXCollaborators: Guibas (CS), Brutlag (Biochemistry), Levitt (Structural Biology), Pande (Chemistry), Lee (Cellular B.) • Surgical simulation (deformable tissue, suturing, visual and haptic feedback)Funding: NSF, NIH, BioXCollaborators: Salisbury (CS+Surgery), Girod (Surgery), Krummel (Surgery) • Modeling and simulation of deformable objectsFunding: NSF-ITR (with UPenn and Rice)Collaborators: Guibas (CS), Fedkiw (CS)
Tadjikistan Pakistan Afghanistan Third Pillar of Dana (California) Cho-Oyu, 8200m, ~27,000ft (Tibet) Muztagh Ata, 7,600m, 25,000ft (Xinjiang, China) Thailand
Rock-Climbing Robot With Tim Bretl and Prof. Steve Rock
Half-Dome, NW Face, Summer of 2010 … Tim Bretl