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Motion Algorithms
Jean-Claude Latombeai.stanford.edu/~latombe
1
Basic QuestionCan two given points be connected by a path in a feasible space?
Feasible space
Forbidden space2
Configuration Space
Issues:• Space dimensionality• Geometric complexity
q1
q3
q0
qn
q4
3
4
q1
q3
q0
qn
q4
Any moving object maps to a point in its configuration space
6 D
~40 D
12 D
15 D
~65-120 D100’s D
feasible space
Probabilistic Sampling
5
F1 F2
Connectivity Issue
Lookout of F1
The β-lookout of a subset X of F is the set of all configurations in X that see a β-fraction of F\X
β-lookout(X) = {q ∈ X | μ(V(q)\X) ≥β×μ(F\X)}
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F1 F2
(ε,α,β)-Expansiveness of F
Lookout of F1
F is (ε,α,β)-expansive if it is ε-good and each one of its subsets X has a β-lookout whose volume is at least α×μ(X)
The β-lookout of a subset X of F is the set of all configurations in X that see a β-fraction of F\X
β-lookout(X) = {q ∈ X | μ(V(q)\X) ≥β×μ(F\X)}
7[Hsu et al., 1997]
Expansiveness only depends on volumetric ratiosIt is not directly related to the dimensionality of the configuration space
In 2-D the expansiveness of the free space can be made arbitrarily poor
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Theoretical Convergence of PRM Planning
Let F be (ε,α,β)-expansive, and s and g be two configurations in the same component of F. BasicPRM(s,g,N) with uniform sampling returns a path between s and g with probability converging to 1 at an exponential rate as N increases
γ = Pr(Failure) ≤
Experimental convergence
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Most narrow passages in F are intentional …
… but it is not easy to intentionally create complex narrow passages in F
Alpha puzzle
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Automatic robot programming
Assembly planning
Space robotics
Reconfigurablerobots
Crowd simulation(egress)
Building codeverification
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Surgicalplanning
Surgicalsimulation
Digital actors
Climbing and legged robots
Proteinmotion 12
Current projectsai.stanford.edu/~latombe/projects/projects-www.html
Development of a fully autonomous climbing robot
Study of the long-timescale dynamics of protein
13
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Autonomous Climbing Robots
Lemur Capuchin
Motion of a Protein
10-15
femtosec10-12
picosec10-9
nanosec10-6
microsec10-3
millisec100
seconds
Bond/atomic vibration
Waterdynamics
Helixforms
Fast anisotropicconf change
long MD run
where weneed to be
MDstep
where we’dlove to be
Slowconf change
one-day MD run
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Approaches1. Experimental approach: Model discrete
heterogeneity in proteins from X-Ray crystallography data
2. Geometric/kinematic approach: Explore reachable conformation space under geometric/kinematic constraints
3. Markov model approach: Model long timescale dynamics with Hidden Markov Models trained from Molecular Dynamics simulation trajectories
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X-Ray Crystallography
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Multiple states lead to ambiguous density
A323Hist
A316Ser
A
B
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Multi-Conformer Approach
1. Sample a huge set C of candidate conformers
2. Select the subset M that collectively best models the electron density map (EDM)
19
Multi-Conformer Approach
1. Sample a huge set C of candidate conformers
2. Select the subset M that collectively best models the electron density map (EDM)
20
Multi-Conformer Approach
1. Sample a huge set C of candidate conformers
2. Select the subset M that collectively best models the electron density map (EDM)
21
Multi-Conformer Approach was Used to produce PDB ID: 3eo6
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