algorithmic robotics and motion...
TRANSCRIPT
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Algorithmic Roboticsand Motion Planning
Dan Halperin
School of Computer Science
Tel Aviv UniversityFall 2019-2020
Multi robot motion planning: Extended review
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Alternative settings/approaches
• distributed, swarm
• the discrete version: MAPF= multi agent path finding
• machine learning
we will review central-control algorithms in continuous domains
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Motion planning:the basic problem
Let B be a system (the robot/s) with k degrees of freedom moving in a known environment cluttered with obstacles. Given free start and goal placements for B decide whether there is a collision free motion for B from start to goal and if so plan such a motion.
Two key terms: (i) degrees of freedom (dof), and (ii) configuration space
(6 robots, 18 dof)
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Review overview
• motion planning, an ultra brief history, hard-vs-easy perspective
• Hard vs. easy:
unlabeled motion planning for many discs
• multi-robot planning in tight settings
• summary and outlook
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Motion planning, an ultra brief history
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Complete solutions
• the problem is hard when the number ofdegrees of freedom (# dof) is part of the input [Reif 79], [Hopcroft et al. 84], …
• cell decomposition the Piano movers series [Schwartz-Sharir 83]: a doubly-exponential solution
• roadmap [Canny 87], [Basu-Pollack-Roy]:a singly-exponential solution
• few dof: very efficient, near-optimal, solutions (mid 80s – mid 90s)
[LaValle]
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# d
of
3
2
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Meanwhile in robotics
• potential field methods [Khatib 86]attractive potential (goal), repulsive potential (obstacles)
• random path planner (RPP)[Barraquand-Latombe 90]
• and then, around 1995PRM (Probabilistic RoadMaps)[Kavraki, Svestka, Latombe,Overmars]
• RRT (Rapidly Exploring Random Trees) [LaValle-Kuffner 99]
• many variants followed• numerous uses, also for many dof
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Hard or easy?
• when is motion planning hard or easy?
• (modern) folklore: it’s hard when there are narrow passages in the C-space on the way to the goal
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clutteredness
3
2
# d
of
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The role of clearance
• probabilistic completeness proofs require an empty sleeve around the solution path• the needed number of samples is inversely proportional to the width of this
empty sleeve
• it seems equally hard to compute this width a priori
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Hard vs. easy: Unlabeled motion planning for many discs
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k-Color multi robot motion planning
• m robots arranged in k groups
• The extreme cases:• k=m, the standard, fully colored problem
• k=1, the unlabeled case
• [Kloder and Hutchinson T-RO 2006]
• [Turpin-Mohta-Michael-Kumar
AR 2014 (ICRA 2013)]
[Solovey-H, WAFR 2012, IJRR 2014]
m=7, k=3
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Unlabeled motion planning
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Unlabeled discs in the plane: the problem
Plan the motion from start to goal:
• 𝑚 interchangeable unit disc robots
• moving inside a simple polygon with 𝑛 sides
• each of the m goal positions needs to be occupied by some robot at the end of the motion
• the robots at the start and goal positions are pair-wise 2 units apart, or 4 unit apart from center to center
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Unlabeled discs in the plane: the problem
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Unlabeled discs in the plane: the solution
A complete combinatorial algorithm running in
𝑂(𝑛 log 𝑛 +𝑚𝑛 +𝑚2) time, 𝑚 is the number of robots and 𝑛 is the complexity of the polygon
[Adler-de Berg-H-Solovey, WAFR 2014, IEEE T-ASE 2015]
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Unlabeled discs in the plane: the solution
A complete combinatorial algorithm running in
𝑂(𝑛 log 𝑛 +𝑚𝑛 +𝑚2) time, 𝑚 is the number of robots and 𝑛 is the complexity of the polygon
F is the free space of a
single robot, F = ⋃i Fi
[Adler-de Berg-H-Solovey, WAFR 2014, IEEE T-ASE 2015]
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Unlabeled discs in the plane: behind the scenes
• nice behavior in a single connected component of F
• impossibility of cycle of effects between connected components >> topological order of handling components
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Unlabeled discs in the plane: why is it (so) easy?
because the workspace is homeomorphic to a disc?
because it is the unlabeled variant?
because the robots are so simple?
because of the separation assumption?
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Because the workspace is homeomorphic to a disc?
NO
Motion planning for discs
in a simple polygon is
NP-hard [Spirakis-Yap 1984]
Reduction from the strong NP-C 3-partition
Labeled, different radii
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Because it is the unlabeled variant?
NO
Motion planning for unlabeled
unit squares in the plane is
PSPACE-hard
[Solovey-H RSS 2015 best student paper award,
IJRR 2016]
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PSPACE-hardness, cont’d
• the first hardness result for unlabeled motion planning
• applies as well to labeled motion planning: the first multi-robot hardness result that uses only one type of robot geometry
• four variants, including “move any robot to a single target”
[Solovey-H RSS 2015 best student paper,IJRR 2016]
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side note
a powerful gem:
PSPACE-Completeness of Sliding-Block Puzzles and other
Problems through the Nondeterministic Constraint Logic
Model of Computation
[Hearn and Demaine 2005]
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Because the robots are so simple?
NO
Motion planning for unlabeled
unit squares in the plane is
PSPACE-hard
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Because of the separation assumption?
YES
• Recall that • the separation relates to two static configurations and not to a full path
• no clearance from the obstacles is required
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An exercise in separation
• a side effect of the analysis [Adler et al] is a simple decision procedure: there is a solution iff in each Fi (connected component of the free space) there is an equal number of start and goal positions
• Q: what is the minimum separation distance λ that guarantees a solution?
• A: 4√2-2 (≈3.646) ≤ λ ≤ 4[Adler-de Berg-H-Solovey, T-ASE 2015]
• new A: λ = 4[Bringmann, 2018]
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Challenges
• Q I: Does the unlabeled hardness proof still hold for unit discs (instead of unit squares)?
• Q II: Is it possible to solve the problem with separation 2+epsilon in time polynomial in m,n, and 1/epsilon?
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Multi-robot planning in tight settings
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Compactifying a multi-robot packaging station
• Before: disjoint workspaces
• After: overlapping workspaces
• Real-time collision detection [van Zon et al CASE 2015]
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Multi robot,complex settings
• Common belief: view as a compound robot with many dofs and use single-robot sampling-based planning to solve coordinated motion problems
modest roadmap with 1K nodes per robot means tensor product for 6 robots with quintillion nodes
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dRRT, slides by Kiril Solovey ,5-13
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Complex multi-robot settings
• Discrete RRT (dRRT)
[Solovey-Salzman-H WAFR 2014, IJRR 2016]
[probabilistic completeness]
• M*
[Wagner-Choset IROS 2010, AI 2015]
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Complex multi-robot settings, cont’d
dRRT*
• Asymptotically optimal [KF11] version of dRRT
[Dobson et al, MRS 2017, best paper award]
• Applied for dual-arm object re-arrangement
[Shome et al, 2018]
clip72 > sec 37
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Side noteEffective metrics for multi-robot motion-planning
• When are two multi-robot configurations close by?
• Metric is key to guaranteeing probabilistic completeness and asymptotic optimality
• Novel metrics tailored to multi-robot planning
• Tools to assess the efficacy of metrics
[Atias-Solovey-H RSS 2017, IJRR 2018]
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Multiple unit balls in Rd
• Fully colored, decoupled (prioritized)
• Revolving areas with non-trivial separation
• Handling hundreds of discs in seconds,
• Finding the optimal order of execution in decoupled
algorithms that locally solve interferences is NP-hard
[Solomon-H WAFR 2018]clip18
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Optimality guarantees in unlabeled multi-robot planning
• Each result requires some extra separation and other conditions
• [Turpin-Mohta-Michael-Kumar AR 2014]:
optimizing min-max
• [Solovey-Yu-Zamir-H RSS 2015]:
optimizing total travel, approx.
assuming 4 separation as before
and minimum distance of start/goal to obstacles
• discrete version pebble problems on graphs [Yu and LaValle]
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Optimizing total travel in unlabeled multi-robot planning, cont’d
• full fledged exact implementation using for free space computation: arrangements, Minkowski sums, point location, etc.
[Solovey-Yu-Zamir-H RSS 2015]
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Multi-robot?How about two robots?
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Coordinating the motion of two discs in the plane
• Problem: Given two (unit) discs moving in the plane among polygonal obstacles, plan a joint free motion from start to goal of minimum total path length
• Efficient algorithm?
• Hardness?
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Coordinating the motion of two discs in the plane,cont’d
• Characterization of optimal paths in the absence of obstacles (Reeds-Shepp style) [Kirkpatrick-Liu 2016]: at most six [straight,circular arc] segments
• Adaptation to translating squares [H-Ruiz-Sacristan-Silveira 2019]
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Rigid motion of two polygons:The limits of sampling-based planning
• Each robot translates and rotates: system w/ 6 dofs
• Start position in bright colors, goal in pale colors
• Pacman needs to swallow the square before rotating to target
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Rigid motion of two polygons, cont’d
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MMS: Motion planning via manifold samples[Salzman-Hemmer-Raveh-H Algorithmica 2013]
Example: polygon translating and rotating among polygons
• sampling the 3D configuration space by strong geometric primitives, including exact arrangements of curves
• combinatorial analysis of primitives yields free space cells
• path planning by intersecting free space cells
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side notek-handed assembly planning and multi-robot
[Salzman-Hemmer-H] [Snoeyink-Stolfi] [Natarajan/Wilson]
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Summary and outlook
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Tools for MRMP
• Multi two-dimensional robots, with separation: complete deterministic algorithms, CGAL
• Complex robot, complex environment: sampling based planners, probabilistic completeness, asymptotic optimality, OMPL
• Multi complex robots: sampling based planners, probabilistic completeness, asymptotic optimality
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Challenges
• Predictive analysis for finite time, which will interpolate between easy and hard
• Identifying the inherent difficulties in multi-robot motion planning
• Optimality!
• Assembly planning, k-handed
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clutteredness
3
2
# d
of
?SB
P
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References: SB planners for multi robot
• Petr Svestka, Mark H. Overmars: Coordinated path planning for multiple robots. Robotics and Autonomous Systems 23(3): 125-152 (1998)
• (M*) Glenn Wagner, Howie Choset: Subdimensional expansion for multirobot path planning. Artif. Intell. 219: 1-24 (2015)
• (dRRT) Kiril Solovey, Oren Salzman, Dan Halperin: Finding a needle in an exponential haystack: Discrete RRT for exploration of implicit roadmaps in multi-robot motion planning. I. J. Robotics Res. 35(5): 501-513 (2016)
• Rahul Shome, Kiril Solovey, Andrew Dobson, Dan Halperin, Kostas E. Bekris: dRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning. CoRR abs/1903.00994 (2019).Also in Autonomous Robots.
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References, cont’d
• Aviel Atias, Kiril Solovey, Oren Salzman, Dan Halperin: Effective metrics for multi-robot motion-planning. I. J. Robotics Res. 37(13-14) (2018)
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THE END