Optimal Multi-Robot Path Planning on Graphs: Structure...
Transcript of Optimal Multi-Robot Path Planning on Graphs: Structure...
Optimal Multi-Robot Path Planning on Graphs: Structure, Complexity, Algorithms,
and Applications
CASE 2016 Multi-Robot Workshop
Jingjin Yu
Computer Science | Rutgers
Outline
Problem Statement Structural Properties of Optimal Formulations Complexity of Non-Optimal Fomulations Complexity of Optimal Formulations
General Graphs Planar Graphs
An ILP Based Novel Solution Application to the Continuous Domain Conclusion The microMVP Platform
Problem Statement
Forbidden moves
1 2
1 2𝐺 = (𝑉, 𝐸)
1
3 4
2
𝑋𝐼
13
4
2
𝑋𝐺
MPP Problem: (𝐺, 𝑋𝐼, 𝑋𝐺), solution: collision free 𝑃 = {𝑝1, … , 𝑝𝑛}
Optimality objectives (minimization):
Max time (makespan): min𝑃∈𝒫
max𝑝𝑖∈𝑃
𝑡𝑖𝑚𝑒(𝑝𝑖)
Total time: min𝑃∈𝒫
σ𝑝𝑖∈𝑃𝑡𝑖𝑚𝑒(𝑝𝑖)
Max distance: min𝑃∈𝒫
max𝑝𝑖∈𝑃
𝑙𝑒𝑛𝑔𝑡ℎ(𝑝𝑖)
Total distance: min𝑃∈𝒫
σ𝑝𝑖∈𝑃𝑙𝑒𝑛𝑔𝑡ℎ(𝑝𝑖)
ApplicationsApplications
Structural Properties of Optimal Formulations
1
1
32
23
𝑥
Makespan Total time
Clockwise
Counterclockwise
𝑥 + 1
𝑥 + 4 𝑥 + 12
2𝑥 + 3
Total distance Total time
Left path only
Using right path
4𝑥 + 14
4𝑥 + 134𝑥 + 10
4𝑥 + 8
2 3
41
1
3
4
2
𝑥 𝑥
Theorem. A pair of the four MPP objectives on makespan, total time, max distance, and total distance demonstrates a Pareto-optimal structure.
Y-LaValle, Arxiv 1507.03289
Complexity of Non-Optimal Formulation
Feasibility is not guaranteed
Theorem. Feasibility of MPP can be decided in linear time. Moreover, a solution for a feasible instance can be computed in 𝐜𝐮𝐛𝐢𝐜 time.
1
4
325
6
7
9
8
256
97
1 8
3
4
?
1 2 2 1×
What about finding optimal solutions?
Y-Rus, WAFR’14
Intractability of Time Optimal MPP
𝒄𝟑𝒄𝟐𝒄𝟏
Theorem. Min Makespan MPP is NP-hard.
𝑥1 ∨ ¬𝑥3 ∨ 𝑥4 ∧ ¬𝑥1 ∨ 𝑥2 ∨ ¬𝑥4 ∧ (¬𝑥2 ∨ 𝑥3 ∨ 𝑥4)3SAT
Y-LaValle, AAAI ’13
𝑥1
𝑥4
𝑥3
𝑥2
𝑐1 𝑐2 𝑐3
𝑛 = 4 variables𝑚 = 3 clauses
𝒄𝟏 𝒄𝟐 𝒄𝟑
𝑚
Min Makespan MPP
𝒙𝟒
𝒙𝟑
𝒙𝟐
𝒙𝟏 𝒙𝟏
𝒙𝟐
𝒙𝟑
𝒙𝟒
Intractability Distance Optimal MPP
Theorem. MPP is NP-hard when optimizing min makespan, min total time, minmax distance, and min total distance.
NP-hardness of distance optimal MPP is slightly more tricky…
Y-LaValle, Arxiv 1507.03289
The Planar Case (𝐺 is Planar)
Planar Monotone 3-SAT
Theorem. Optimal Planar MPP (PMPP) is NP-hard for min makespan,min total time, min max distance, and min total distance objectives.
𝑥1 ∨ 𝑥4 ∨ 𝑥5∧ (𝑥2 ∨ 𝑥3)∧ ¬𝑥1 ∨ ¬𝑥2 ∨ ¬𝑥3∧ ¬𝑥3 ∨ ¬𝑥4 ∨ ¬𝑥5
𝑐1𝑐2𝑐3𝑐4
Planar MPP
𝒄𝟒𝒄𝟑
𝒄𝟐
𝒄𝟏
𝒄𝟏
𝒄𝟐
𝒄𝟑 𝒄𝟒
Y, IEEE RA-L, 2016
Practical Implications
Polynomial time exact solution Polynomial time suboptimal solutions?
Engineering the environment helps Two way, multi-lane roads Elevated intersections
Optimal MPP and PMPP are often NP-hard
𝒄𝟒𝒄𝟑
𝒄𝟐
𝒄𝟏
𝒄𝟏
𝒄𝟐
𝒄𝟑 𝒄𝟒
We offer rigorous, quantitative justifications of these phenomena through complexity theory
Approaches Based on Discrete Search
Discrete approaches are mostly A* based The global search space is 𝐺 𝑛
Differ on the handling of robot interactions
Local Repair A* (LRA*) [Zelinsky, IEEE TRA’92] Planning for each robot and resolving conflicts locally as they appear
Windowed Hierarchical Cooperative A* (WHCA*) [Sliver, AIIDE’05] Local space-time window for handling multi-robot interactions
IDA* based [Sharon-Stern-Felner-Sturtevant, AAAI’12] Iteratively handle more robot-robot interaction
Maximum Group Size (MGSn) [Standley-Korf, IJCAI’11] Grouping robots into larger bundles as necessary The authors pushed a highly effective implementation
Many additional approaches: [Ryan’08], [van Den Berg et al.‘09], [Wagner-Choset’11], [Boyarski et al.’15],…
𝑛 independent robots: 𝒏|𝑮| joint search space: 𝑮 𝒏
An Integer Programming Based Novel Approach
Key idea: time expansion
1 1’ 2 2’t = 0 T=4 4’3 3’
𝑟1 𝑟2′
𝑟2𝑟1′
12
3
4
2
1
3,4
Theorem. Fixing a natural number 𝑇, a MPP instance admits a solution with atmost 𝑇 time steps if and only if the corresponding time-expanded networkwith 𝑇 periods admits a solution consisting of disjoint paths.
Y-LaValle, WAFR’12
ILP Approach: The Constraints
𝑢 𝑣
Meet-on-edge
……
……
……
……
……
……
𝑢
𝑤
𝑣
𝑡 𝑡 + 1
1≤𝑖≤𝑛
(𝑥𝑢𝑣,𝑡,𝑡+1𝑖 + 𝑥𝑣𝑣,𝑡,𝑡+1
𝑖 + 𝑥𝑤𝑣,𝑡,𝑡+1𝑖 ) ≤ 1
1 2
……
……
……
……
𝑢
𝑣
𝑡 𝑡 + 1
1≤𝑖≤𝑛
(𝑥𝑢𝑣,𝑡,𝑡+1𝑖 + 𝑥𝑣𝑢,𝑡,𝑡+1
𝑖 ) ≤ 1
𝑢 𝑤𝑣
Meet-on-vertex
1 2
Algorithm for Min MakespanY-LaValle, ICRA’13, TRO-16, in press, online
Pick an initial 𝑇
No 𝑇 = 𝑇 + 1
Yes
Return the path set
Feasible? Run optimizer
Additional heuristics Reachability analysis Divide and conquer
Other objectives (total time, max distance, total distance)
Build the time-expanded network
1 1’0 2 2’ 3 3’ 4 4’
Set up an ILP model
𝑚𝑎𝑥
𝑖=1
𝑛
𝑥𝑖,𝑖 , 𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜
∀𝑒𝑗 ,
𝑖=1
𝑛
𝑥𝑖,𝑗 ≤ 1
∀𝑣,
𝑒𝑗∈𝛿+(𝑣)
𝑥𝑖,𝑗 =
𝑒𝑗∈𝛿−(𝑣)
𝑥𝑖,𝑗
Performance – Makespan
24x18 grid, with some vertices randomly removed to simulate obstacles
Exact solution Near-optimal solution
44% robot density
Performance – Total Time and Total Distance
Min Total Time Min Total Distance
We Can Solve Some Tough Problems…
1025 states> 104 branching factor
13 17 144 23
1 22 129 7
11 16 815 21
25 24 196 20
10 3 25 18
1 2 43 5
6 7 98 10
11 12 1413 15
16 17 1918 20
21 22 2423 25
A 7-step min makespan plan
Generalization to Continuous DomainY-Rus, ISRR’15
Lattice overlay Restore connectivity
Snapping Trajectory planning Path smoothing
Conclusion
Contributions
Structure and complexity
MPP and PMPP appear NP-hard in general
Algorithmic solution
Effective integer programming based solution approach
Extensible to continuous problems
Future work
Algorithmic solutions for more realistic setup
Continuously appearing start and goal locations
Various constraints
Effective environment design
“Almost planar” design with guaranteed traffic throughput
Planar graph for doing the same?
Remove the ILP dependency
microMVP (micro Multi-Vehicle Platform)
An open platform targeting robotics research and education Low cost (currently <$100 per vehicle, $100 for the sensing “platform”)
Easy assembly 20min to build a vehicle – enabled by 3D printing
1 minute to setup the platform
Small scale (in my backpack!)
Suitable for deployment everywhere
Open (very soon)