Mobile Robot Motion Planning Outline
Last modified: Wed, September 19, 2001
Outline Pointers
I
Intro
II
Bug Algorithms
III
Configuration Space, Part I
IV
Potential Functions
V
Configuration Space, Part II
VI
Pixel-Based Maps, Part I
VII
RoadMaps
VIII
Probabilistic Motion Planning Techniques
IX
Pixel-Based Maps, Part II
X
Simultaneous Localization and Mapping
XI
Visibility Based
XII
Coverage
XIII
Non-holonomic Path Planning
XIV
Visual Servoing
XV
Trajectory planning
XVI
Nonholonomic motion planning
XVII
Manipulation planning
XVIII
Miscellaneous
Outline wo/papers
Research Links
Author web pages
references start
I.
Intro
(Text:
pdf
/
ps
, (last updated 9/5/2001, Copyright Howie Choset, 2001)
Where am I?--Sensors and Methods for Mobile Robot Positioning
by J. Borenstein , H. R. Everett , and L. Feng
Robot Motion Planning
by J.C. Latombe (out of print)
Introduction to AI Robotics
by Robin Murphy
Sensors for Mobile Robots
by H. Bart R. Everett
Computational Principles of Mobile Robotics
by Gregory Dudek and Michael Jenkin
Computational Geometry: Algorithms and Applications
by
Mark de Berg
,
Marc van Kreveld
,
Mark Overmars
,
Otfried Schwarzkopf
Computational Geometry in C (Second Edition)
by Joseph O'Rourke
II.
Bug Algorithms
(Text:
pdf
/
ps
, last updated 9/5/2001, Copyright Howie Choset, 2001)
A. Bug2
Lumelsky
1. Completeness
2. Wall Following: Numeric Continuation Methods
3. Centerline Sonar Model: distance to closest obstacle
4. Does the robot really reach the goal?
D. Fox, W. Burgard, and S. Thrun
B. TanBug (
Rimon
)
C. WedgeBug (
Laubach & Burdick
)
D. 3D Bug (
Rimon
)
III.
Configuration Space, Part I
Configuration Space (
Lozano-Perez
)
IV.
Potential Functions
A. Attractive/Repulsive Functions
1. Positive Charge Analogy and Hill Climbing
2. Distance Function
Definition and gradient
Centerline Sonar Model: Distance to all obstacles
Local Minima in sensor array
Brushfire algorithm
3. Potential Function Definition
Functional Definition
Gradient Descent (Minima, maxima, saddle)
Direct Sensor Measurements
Vectorfield Histogram (
Borenstein
)
Brushfire
Fast March Algorithm (
Sethian
(Potential based))
4. Combination of Functions (related to behavioral-based)
Piece-wise potential functions (
Arkin/Balch
"Aura: Principles and Practices" - adding discrete potential fields)
B. Local Minimum Problem -- trapped
C. Adding more forces (
Balch/Arkin
"Avoiding the past" - method to overcome local minima)
D. Navigation Functions (
Rimon & Kodicheck
)
E. Randomized Potentials??? (Here or probabilistic techniques chap) (
Barraquand, Langlois & Latombe
)
V.
Configuration Space, Part II
A. Polygonal Obstacles (
De Berg etc. Chapter 13
)
B. Some higher dims
VI.
Pixel-Based Maps, Part I
A. Search a grid (Advertise Numerical Recipes)
1. Binary search, depth-first, breadth first
2. Iterative depth first deepening (combo of depth first and breadth first) (
Nourbash
-
(no papers)
)
3. Dijkstra's Algorithm
4. A* (some basic thing)
5. D*
Stentz
Balch
"Grid-based Navigation for Mobile Robots" - brushfire applied to occupancy grid
B. Frontier Exploration
Yamauchi
Simmons
C. Maybe Borenstein here....? (
Borenstein
)
D. Framed Quad-Trees (
Stentz
)
VII.
Roadmaps
A. Visibility Graph
B. Canny's Roadmap (
Canny
)
C. OPP
1.
Canny & Lin
2.
Rimon
D. GVG
1.
Rao
2.
Choset
E. Place Maps
1.
Kuipers
2.
CJ Taylor and Kriegman
VIII.
Probabilistic Motion Planning Techniques
A. Probability Primer (Howie wrote this)
B. Probabilistic potential functions (??? here or above) (
Latombe
)
C. Roadmaps
Kavraki
Latombe
Amato
Gupta
D. RRT's
Lavalle & Kuffner
IX.
Pixel-Based Maps, Part II
A. Probability Primer
B. Occupancy
Morevac
Elfes
Konolidge
(Improved Occ)
Kleeman
C. Hybrid Approaches
Thrun
Kortenkamp
Arras
- (finding line segments from collections of points like Hough)
X.
Simultaneous Localization and Mapping
A. Kalman
text that howie wrote
Davison
(slam using vision)
Leonard and Durrante-Whyte
Guivant
(slam - ekf)
B. Bayesian
Burgard
(Markov Localization)
Fox
(Monte Carlo Localization)(Particle Filter)
Thrun
Konolidge
(Markov Localization using Correlation )
C. Topological Localization
Choset
Kuipers
D. Particle Filter Techniques (
Isard
Condenstation algorithm for vision)
Where? (
Deans
(Invariant Filter))
XI.
Visibility Based
A. Art Gallery (
O'Rourke
-
(no papers)
)
B. Voronoi Diagram (
Choset
)
C. Pursuer-Evader (
Lavalle
)
XII.
Coverage (Cell Decompositions)
A. Cellular Decompompositions
O'Rourke
- CG Ch2
B. Boustrophedon and Related Approaches
Choset
Acar
Prasad
Lumelsky
Hert
Rimon
XII.
Coverage (Cell Decompositions)
A. Trapezoid (Chazzelle or basic thing)
Chazzelle
-
(no papers)
O'Rourke
- CG Ch2
B. Boustrophedon and Related Approaches
Acar
1. include Bug 2.5 here
C. Cellular Decompomposition (
Collins
-
(no papers)
)
D. Approximate Cell Decomps and Coverage Algos (
Butler
(where?))
XIII.
Non-holonomic Path Planning
XIV.
Visual Servoing
Arras
- (laser and visual homing)
XV.
Trajectory planning
path + time-scaling = trajectory
robot dynamics
fully actuated vs. underactuated (more on underactuated next chapter)
kinodynamic (Canny, Donald, Reif, Xavier...)
RRT (LaValle and Kuffner)
decoupled approach: path planning followed by time-scaling (Bobrow et al., Shin and McKay, Shiller, Hollerbach, ...)
optimization approaches (Tan and Potts, Vukobratovic, many using SQP like Bobrow)
XVI.
Nonholonomic motion planning
(restrict to R^n, leave out Lie groups, coordinate-free formalisms, appendix?)
tangent vector, tangent space, tangent bundle
vector fields, integral curves, Lie bracket
distributions, involutivity, integral manifold, Frobenius
dynamical polysystem, control affine systems, drift and drift-free
controllability definitions
Lie algebra of vector fields, LARC
STLC
nonholonomic constraints: (Pfaffian) rolling, conservation of momentum
positive formulation: vector fields; negative: constraints
underactuated robots (2nd order constraints)
- motion planning for cars, cars and trailers (Latombe, Laumond, Lumelsky IROS 2000, lots...)
- holonomic planning followed by transform for STLC systems
- optimization approaches, nonsingular loops (Wen, Sontag, Sussmann, Fernandes, others)
- geometric phase, iterative closed loops
- RRT
- differentially flat systems (Murray, Fliess, Martin, others)
- chained form systems (many)
- kinematically controllable systems (Bullo and Lynch)
- averaging/oscillatory approaches
(- mixed kinematic and dynamic)
- fictitious inputs, Lafferiere's method
some other references: Latombe book, Canny and Li book, Murray book, Mason book, Wen article, my ME 495 lectures
XVII.
Manipulation planning
(Lots possible here: need to define scope; below is very rough)
adding manipulation mechanics to planning
manipulation taxonomy (Mason and Lynch)
movable objects (Wilfong)
transit paths and transfer paths (Alami, Dacre-Wright, Koga, Kuffner, Ferbach ...)
assembly/disassembly planning, NDBG's, etc. (Wilson, many)
friction, wrenches, moment labels, form and force closure
grasp planning (including 2nd order mobility, Rimon and Burdick)
(possibly finger gaiting, Rus-Zefran-Goodwine etc., rolling, but don't digress into control)
push planning and controllability of manipulation (Lynch)
other push planning (Sudsang and Ponce, Agarwal)
grasp sequences (Brost, Goldberg), conveyor feeding
LMT
AND/OR graphs
XVIII.
Miscellaneous
Return to the bug algorithm in coverage