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Analysis of Greedy Robot-Navigation Methods Sven Koenig (USC) Apurva Mugdal (Ga. Tech) Craig Tovey (Ga. Tech) Localization Goal-based Planning Given map of 2D or 3D gridworld, determine location Given map minus roadblocks, reach goal Move the robot to solve: Graph Model of Robot Motion

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analysis of greedy robot navigation methods

Analysis of Greedy Robot-Navigation Methods

Sven Koenig (USC)

Apurva Mugdal (Ga. Tech)

Craig Tovey (Ga. Tech)

move the robot to solve
Localization

Goal-based Planning

Given map of 2D or 3D gridworld, determine location

Given map minus roadblocks, reach goal

Move the robot to solve:
graph model of robot motion
Graph Model of Robot Motion
  • Occupy one vertex of G=(V,E) at a time.
  • G usually is a gridgraph:
  • V= set of cells.
  • Adjacency is {N,S,E,W} or chess king.
  • Robot has compass
  • Tactile sensors detect neighbors of current vertex v; other sensors may detect more.
1 st problem localization
1st problem: localization
  • Know graph G (input).
  • Robot is at some vertex of G.
  • Problem: determine location by moving about, making observations at each vertex visited.
  • Might conclude that robot cannot uniquely determine its location.
2 nd problem goal directed search target
2nd problem: goal directed search (target)
  • Know graph G=(V,E)
  • Know initial location s and goal t
  • Robot has compass, or on general graphs, can distinguish among vertices
  • Don’t know B V, set of blocked vertices
  • If w is blocked and (v,w) E, the robot detects that w is blocked when it scans from v
  • There may be no unblocked s to t path
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Robots are slow

Planning time usually small compared with travel time

We can replan as we gain information

Our plans can be algorithms

greed goes by many names
Greed goes by many names…
  • D* [Stentz 95];
  • D-Lite [Koenig-Likhachev 02]
  • Greedy mapping [Thrun et al. 98]
  • A*
  • Planning with Freespace Assumption
greed is found in many places
Greed is found in many places…
  • Nomad class museum tour-guide[Thrun et al. 98]
  • Nomad 150 mobile robots [Koenig-Likhachev 02]
  • Super Scouts [Romero et al. 01]
  • Mars Rover Prototype
  • Nourbakhsh and Genesereth[96]
but it is always the same idea

but it is always the same idea

Choose the most economical move that improves the situation

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Target: move along a shortest presumed unblocked path to t.
  • Localization: move to the nearest vertex which if scanned eliminates at least one location from set of remaining possibilities. If you don’t know whether or not you can get to it, it isn’t the nearest vertex that reduces uncertainty.
slide18

Mars

Rover

Prototype

main results on greedy algorithms
Main results on greedy algorithms

upper bound: goal search on general graphs

greed upper bounds on travel
Greed: upper bounds on travel
  • Localization -- O(n log n) bound by covering region with bomb blasts. Applies to greedy mapping too.
  • Target: localization analysis does not apply. Use bounds on girth of graphs instead.
analyzing greedy localization for any sensor type
Analyzing greedy localization for any sensor type
  • Algorithm travels to a nearest informativevertex.
  • That vertex is scanned and becomes uninformative.
  • Other vertices may become uninformative too.
  • Uninformativevertices never become informative.
if you take a large step all the vertices in a large area are not informative
If You Take a Large Step, All the Vertices in a Large Area Are Not Informative
  • Define a bombing sequence as a sequence of (vertex, radius, unbombed set) triplets.
  • Drop a bomb on an unbombed vertex with the given blast radius.
  • The adversary maximizes the sum of the blast radii (+1)
benefits of greedy localization analysis
Benefits of Greedy Localization Analysis
  • Most current implementations travel to nearest vertex about which there is uncertainty, rather than to nearest informative vertex, for non-tactile sensors

Same upper bound holds but we suspect performance is slightly worse

  • Applies to greedy mapping too
upper bounds for goal search
Upper Bounds for Goal Search
  • Why bombing sequence does not apply
  • Telescoping
  • Time reversal: add edges to blocked vertices
  • Adding edges makes cycles. Big steps mean big (long) cycles.
  • Relate to bounds on girth (shortest cycle) from Euler’s formula, Alon et al’s thm [01].
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Telescopeidea for target

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(10 - 2) + (22 – 6) + … within |V| of total

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Time reversal and cycles

Reverse time: add the failed edges

22 – 6· length shortest cycle containing new edge - 4

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Time reversal and cycles

7 – 6· length shortest cycle containing new edge - 4

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bounding sum of cycle lengths
Bounding sum of cycle lengths
  • IDEA: as we go backwards in time, graph has more edges. Shortest cycle containing new edge should not often be big
  • Girth: length of shortest cycle
  • Thm [Alon, Hoory, Linial]: Any graph with average degree d>2 has girth · logd-1|V|
  • Sorting, etc. gives O(log^2 |V|)
  • Planar graphs: O(log |V|) by considering faces and Euler’s formula
conclusions
Conclusions
  • Robot motion provides a nice blend of theory and practice
  • Some theoretical justification for greed
  • Idea of visiting informative vertices may slightly improve current implementations of greedy localization (and mapping)
  • Informative vertices might be useful for goal search if |B| is not small.
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lower bounds

lower bounds

Essentially one proof for both problems

(Target grid graph construction differs significantly)

slide50

Localization

Make an extra copy for each branch and block the leaf at its tip

X

The robot has to check each tip to know which copy it is in