RRTBlossom

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RRT-Blossom

• RRT with local flood-fill behavior

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Highly-constrained terrain
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Purpose

• highly-constrained environments
• less room to move
• ? smaller search space
• ? motion planning should be easier
• Rapidly-Exploring Random Trees (RRT)
• popular motion planning algorithm
• does poorly in highly-constrained terrain
• RRT-Blossom
• variation of RRT well adapted to such terrain

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Flood-fill traits to emulate

• generally, a flood-fill
• has a constant rate of fill
• does not visit a location more than once
• in RRT context this translates to
• make sure tree gains an edge on each iteration
• do not re-explore the same space twice

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Key modifications

• receding edges
• allow edges that recede from the target point
• re-exploration prevention
• do not revisit same space with multiple nodes
• node blossoming
• avoid duplicate work by immediately and
  permanently keeping or discarding tested edges

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Receding edges

• often provide useful information ? worth keeping
• RRT does not allow receding edges
• RRT-CT allows receding edges, but does not guard
  against resultant re-exploration

P.Cheng S.M.LaValle, Reducing Metric
Sensitivity in Randomized Trajectory Design, IROS
2001
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Re-exploration(regression)

• RRT guarantees no edge/node overlap
• receding edges break this guarantee
• now possible multiple tree nodes exploring same
  space
• re-exploration wasted effort (often huge!)

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Preventing re-exploration

• generally, re-exploration non-trivial to detect
• approximation that works well

• edge regresses if its leaf node is closer to a
  tree node other than its parent

• prevention do not instantiate edges which
  satisfy the above

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Blossoming

• instantiate all valid edges out of chosen node
• avoids duplicate edge computation and testing
• RRT memoryless
• recomputes good bad edges
• RRT-CT remembers only bad edges
• recomputes good edges
• RRT-Blossom remembers all edges
• by instantiating all valid edges out of node, no
  need to remember anything

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Viability issue

• dead-end branches can block needed edges
• can prevent discovery of solution!
• fix for re-exploration check
• ignore such nonviable branches
• viability discovered on-the-fly

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Experiments agents
point
car
bike
kinematic
kinodynamic
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Experiments terrains
T
rooms
complex
tunnel
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Results point(holonomic)
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Results car(nonholonomic)
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Results bike(kinodynamic)
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Take-away

• RRT-Blossom more robust RRT
• highly-constrained terrain ? big speedup
• deep local minima ? big speedup
• regular terrain ? comparable performance
• performs well in both settings
• kinematic
• kinodynamic
• more at

http//www.dgp.toronto.edu/mac/rrt-blossom/