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Bounded Optimal Coordination for
Human-Robot Teams
G. Ayorkor Mills-TetteyThesis Proposal: 12th December 2008
Committee: Anthony Stentz, M. Bernardine Dias,
Michael Trick, Nicola Muscettola
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Motivation
INTRODUCTION
[IMAGE CREDITS]
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem
INTRODUCTION
Compute:• Allocation of tasks to agents• Schedule for each agent• Route for each agent
Maximizing: (rewards for completed tasks)- (travel costs for agents’ routes)- (Cost for agents’ idle time)
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem
INTRODUCTION
Compute:• Allocation of tasks to agents• Schedule for each agent• Route for each agent
Maximizing: (rewards for completed tasks)- (travel costs for agents’ routes)- (Cost for agents’ idle time)
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Problem Features Tasks
Spatially distributed Multi-step Time constraints Location choice Precedence constraints Simultaneity constraints Proximity constraints
Agents Heterogeneous Capacity constraints
Locations Capacity constraints Mutual exclusion constraints
INTRODUCTION
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Thesis
Goal: compute bounded optimal, anytime solution to complex coordination problem
Allocation of tasks to agents Schedule for each agent Route for each agent
Characteristics of application domains: Bounded optimality is important “Small” teams: 10s of agents, 10s-100s of tasks Planning time in minutes (or hours) acceptable Centralized planning acceptable
INTRODUCTION
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Related WorkProblem Features Vehicle
RoutingMulti-Robot Task Allocation
Spatial distribution
Multi-step
Time constraints
Location choice
Precedence constraints
Simultaneity constraints
Proximity constraints
Heterogeneity
Capacity constraints
Capacity constraints
Mutual exclusion constraints
Tasks
Agents
Locations
RELATED WORK
Optimal algorithms Approximate algorithms
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Vehicle Routing Problem
Transportation of passengers / distribution of goods between depots and final users
RELATED WORK
iD
j
l
k
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Vehicle Routing Problem
Mathematical models – mixed integer linear programming 3-index models (e.g. Cordeau, 2006) 2-index / set-partitioning models
(e.g. Savelsbergh & Sol, 1998)
RELATED WORK
iD
j
l
kaijx a
rxrIndicates if an agent a
traverses the edge from node i to node j
Indicates if an agent a performs route r
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Vehicle Routing Problem
Exact approaches Mathematical Programming
Heuristic Approaches Construction and improvement heuristics Tabu Search, Genetic Algorithms, Simulated
Annealing, etc
RELATED WORK
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Vehicle Routing Problem
Problem features not addressed: Precedence & simultaneity constraints with
penalization of waiting/idle time Location choice for tasks Capacity constraints on locations Proximity constraints Mutual exclusion constraints
RELATED WORK
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Multi-Robot Task Allocation
In a multi-robot system, which robot should execute which task?
RELATED WORK
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Multi-Robot Task Allocation
Market-Based Approaches
RELATED WORK
A victim needs to be rescued at location (4, 2)
I can do it for $80
It will cost me $54 $73
$101
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Multi-Robot Task Allocation
Related problem (Jones, 2007) Planning, allocating and scheduling agent interactions for
team tasks with precedence and simultaneity constraints Solution approach: Market-based approach with
combinatorial bidding and tiered auctions Limitations for this thesis:
Does not include all problem features
No optimality guarantees
RELATED WORK
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Multi-Robot Task Allocation
Related problem (Koes, 2005) Planning and Execution for Multirobot Coordination Solution approach: Mathematical programming (constraint
optimization) Limitations for this thesis:
Does not include all problem features Relies on off-the-shelve solvers, solution algorithm not
customized to problem domain
RELATED WORK
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Problem Statement
Given A set of tasks (could be multi-step) A team of heterogeneous agents A set of locations A set of constraints (compatibility, precedence,
simultaneity, proximity, mutual exclusion)
Problem is to determine optimal: Task allocation Schedule Routes
PROBLEM STATEMENT
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Technical Approach
Mathematical programming To guarantee bounded optimality
Specifics: Create a set-partitioning formulation of the
problem, with side constraints Design a branch-and-price algorithm to solve it
TECHNICAL APPROACH
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Technical Approach: Overview
Background Set-partitioning formulation Branch-and-bound Branch-and-price
Application to thesis problem Set-partitioning formulation with side constraints Formulation of pricing sub-problem
TECHNICAL APPROACH
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
ExampleSet-Partitioning Formulation
1P
B
3D
3P
1D
2D
2P
4D
A
4P
TECHNICAL APPROACH
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Set-Partitioning Formulation Example
1P
B
3D
3P
1D
2D
2P
4D
A
4P
TECHNICAL APPROACH
Possible route
r0
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Set-Partitioning Formulation Example
1P
B
3D
3P
1D
2D
2P
4D
A
4P
TECHNICAL APPROACH
Possible route
r1
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Set-Partitioning Formulation Example
1P
B
3D
3P
1D
2D
2P
4D
A
4P
TECHNICAL APPROACH
Possible route
r2
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Set-Partitioning Formulation Example
1P
B
3D
3P
1D
4D
4P r0 r1 r2 r3 r4 r5
A: 0 0 1
B: 0 0 0 0 1
Each agent assigned to at most 1 route:
r0 r1 r2 r3 r4 r5
1: 0 0 0 = 1
2: 0 0 = 1… … … … … … …
Each task assigned to exactly 1 route:
TECHNICAL APPROACH
Set Partitioning Formulation:
Arx1
Arx2
Arx3
Brx0
Brx4
Brx0
Arx3
Brx4
Arx1
Arx2
Arx3
r0
r1
r2
r3
r4
2DA
2P
Arx5
r1 Arx5
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Set of all agents
Set-Partitioning Formulation Example
r0 r1 r2 r3 r4 r5
A: 0 0 1
B: 0 0 0 0 1
Each agent assigned to at most 1 route:
r0 r1 r2 r3 r4 r5
1: 0 0 0 = 1
2: 0 0 = 1… … … … … … …
Each task assigned to exactly 1 route:
Arx1
Arx2
Arx3
Brx0
Brx4
Brx0
Arx3
Brx4
Arx1
Arx2
Arx3
TECHNICAL APPROACH
Set Partitioning Formulation:
kr
krx 1
1 Mk r
kr
kir
k
x
(for each agent, k)
(for each task, i)
Set of all feasible routes for agent k
Indicates if request i is on route r
Arx5
Arx5
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Branch-and-Bound
TECHNICAL APPROACH
P
P1 P2
20.67
22 21
min:Ar
Ar
Br
Br
Ar
Ar
Ar
Ar
Ar
Ar
Br
Br xcxcxcxcxcxc
554433221100
subject to: 15321 A
rAr
Ar
Ar xxxx
140 B
rBr xx
…
}1,0{,,,,,543210A
rBr
Ar
Ar
Ar
Br xxxxxx
)0,,,,,0( 32
31
31
31x
)0,0,0,0,1,1(x)1,1,0,0,0,0(x
Ar
Br
Ar
Ar
Ar
Br xxxxxx
54321071598615
route costs
01A
rx 11A
rx
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Solution
1P
B
3D
3P
1D
2D
2P
4D
A
4P
10B
rx
11A
rx
TECHNICAL APPROACH
r0
r1
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Branch-and-Price
But, there might be too many routes to enumerate explicitly. What to do? Column generation at each node of branch-and-
bound tree:
TECHNICAL APPROACH
P
P1 P2
P11 P12
At each node:• Compute the linear programming (LP)
relaxation with a subset of the routes• Solve a pricing sub-problem to find a
profitable route to include• Re-compute the LP relaxation• Repeat until no profitable routes
r0 r1 r2.
5.45.24.7r3.
r0 r1 r2. r3. r0 r1 r2. r3.
5.3 5.04.8r4.
4.9
r5.
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Background:Branch-and-Price
Pricing sub-problem for this example:
TECHNICAL APPROACH
kNi r
kikir
kr rMkvuc
k
,min
Cost of route r for agent k
Cost modification for each task (node) on the route
Cost modification for entire route
Dual variables
Shortest route problem in a graph with modified node costs!
1 iff task i is on route rk
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Recap Thesis Problem
Problem Features: Tasks:
Agents: Locations:
Technical Approach: Branch-and-price algorithm on set-partitioning
formulation with side constraints
TECHNICAL APPROACH
Spatially distributed, Multi-step, Location choice, Time windows,Precedence, Simultaneity, Mutual exclusion, Proximity constraints
Heterogeneous, Capacity constraints
Capacity constraints, Mutual exclusion constraints
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem
1
A
2
3a
4a
3b
4b
B
C
Silo 1
Silo 2
Field 1
Field 2
5
3b
4b 5
6
6D
TECHNICAL APPROACH
Harvest field 1
Harvest field 2
Move field 1 grain to silo
Move field 2 grain to silo
Monitor field 1 grain unloading
Monitor field 2 grain unloading
1
2
3a 3b
4a 4b
5
6
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem
Task Subtasks Compatible locations
Compatible agents
Harvest field 1 Field 1
Harvest field 2 Field 2
3. Transport field 1 grain Load
Unload
Field 1
Silo 1, Silo 2
4. Transport field 2 grain Load
Unload
Field 2
Silo 1, Silo 2
Monitor field 1 grain unloading
Monitor field 2 grain unloading
A
A
B C
B C
3a
3b
4a
4b
1
2
5
6
D
D
TECHNICAL APPROACH
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example ProblemPrecedence constraints:
Harvest field 1 before Load grain from field 1
Harvest field 2 before Load grain from field 2
Simultaneity constraints:
Monitor field 1 grain unloading concurrent with Unload field 1 grain
Monitor field 2 grain unloading concurrent with Unload field 2 grain
Proximity constraints:
Monitor field 1 grain unloading collated with Unload field 1 grain
Monitor field 2 grain unloading collated with Unload field 2 grain
1
2
3a
3b
3a
4b
5
6
5
6
3b
4b
TECHNICAL APPROACH
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem:Feasible Route
1
2
3.1
4.1
B
C
A
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem:Feasible Route
1
2
3.1
4.1
B
C
A
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem:Feasible Route
1
2
3.1
4.1
B
C
A
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem:Feasible Route
1
2
3.1
4.1
B
C
A
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Set Partitioning Formulation with Side Constraints
Objective Function
1 route per agent 1 route per task
Valid start time for taskValid arrival time for agent for task
Valid idle time for agent for task
Synchronization constraints
Proximity constraints
Precedence constraints
Non-overlapping constraints
Mutual exclusion constraints
Location capacity constraints
Maximize:
Subject to:
TECHNICAL APPROACH
Sid
e co
nstr
aint
sS
tand
ard
set-
part
ition
ing
form
ulat
ion
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Objective Function
Maximize
Rewards for completed tasks
Travel costs for selected routes
Costs for agents’ idle/waiting time
TECHNICAL APPROACH
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Objective Function
Maximize
Mj Kk r
kr
kjrj
k
xr
Kk r
kr
Rkr
k
xc
Ni l Kk
lki
Wk
i
wc
Rewards for completed tasks
Travel costs for selected routes
Costs for agents’ idle/waiting time
Route selection variable: 1 if agent k is assigned route r
Idle/waiting time of agent k for subtask i at location l
TECHNICAL APPROACH
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Set Partitioning Formulation with Side Constraints
Objective Function
1 route per agent 1 route per task
Valid start time for taskValid arrival time for agent for task
Valid idle time for agent for task
Synchronization constraints
Proximity constraints
Precedence constraints
Non-overlapping constraints
Mutual exclusion constraints
Location capacity constraints
Maximize:
Subject to:
TECHNICAL APPROACH
Sid
e co
nstr
aint
sS
tand
ard
set-
part
ition
ing
form
ulat
ion
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Pricing Sub-problemTECHNICAL APPROACH
Reward for each completed task
∆cost for entire route
∆cost for each subtask
linear ∆cost for each subtask
∆cost for each subtask in a precedence/simultaneity constraint
∆cost for each subtask at a capacity-constrained location
∆cost for each subtask in a non-overlapping constraint
∆cost for each subtask at a location with mutual-exclusion
Find route (sequence of subtask/location pairs) that maximizes:
+
route
-Travel cost-
-
-
--
--
Subject to constraints:
Each route includes only 1 location per subtask
All subtasks of a given task are on the same route
∆reward for each completed task -
∆cost for each subtask in a proximity constraint
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Work to Date
Implemented branch-and-bound algorithm for simplified version of thesis problem Includes precedence constraints Does not include other side constraints Explicitly enumerates all routes – does not involve
column generation
WORK TO DATE
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Example Problem: 12 single-step tasks, 5 agents, max 3 tasks/agent
WORK TO DATE
Tasks
Agents
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Example Problem: Optimal Solution 12 single-step tasks, 5 agents, max 3 tasks/agentNo precedence constraints
WORK TO DATE
0 5 10 15 time
Timeline:
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem: Optimal Solution12 single-step tasks, 5 agents, max 3 tasks/agent2 precedence constraints: t4<t8, t9<t8
WORK TO DATE
0 5 10 15 time
Timeline:
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem: Optimal Solution12 single-step tasks, 5 agents, max 3 tasks/agent4 precedence constraints: t4<t8, t9<t8, t8<t5, t5<t3
WORK TO DATE
0 5 10 15 time
Timeline:
Waiting/idle time
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Quantitative Results
WORK TO DATE
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Bounded Optimality:Example with 4 precedence constraints
# Iterations0 100 200 300 400 500
30,000
25,000
20,000
15,000
10,000
5,000
0
So
luti
on
Val
ue
Legend
Best solution found
Upper bound
Found optimal solution
Proved optimality of solution
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Overview of Remaining Work
Develop branch-and-price algorithm Extend implementation to include all side constraints Develop algorithms to solve pricing sub-problem Implement column generation
Develop heuristic algorithms for comparison Evaluate branch-and-price against heuristic
algorithms Solution quality Solution time
WORK TO DATE
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Schedule
CONCLUSION
Dec ’08 – Jan ‘09 Formulate benchmark problems
Jan ‘09 Implement omitted side constraints
Feb – Apr ’09 Design and implement algorithms for pricing sub-problem
Apr – May ’09 Implement column generation
Jun – Jul ’09 Evaluate branch-and-price algorithm, develop comparison heuristic approaches
Aug – Sep ’09 Refine algorithms and continue evaluation
Oct – Nov ’09 Write thesis
Dec ’09 Defend Thesis
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Contributions
First branch-and-price approach to optimal robotic team coordination
Set-partitioning formulation of novel complex coordination problem
First optimal task allocation approach enabling cooperation in human-robot teams
CONCLUSION
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Acknowledgements
Sponsors: Qatar National Research Fund (QNRF) NASA Jet Propulsion Laboratory (JPL)
Thesis Committee: Anthony Stentz, M. Bernardine Dias,
Michael Trick, Nicola Muscettola rCommerce Lab Members Friends and Family
CONCLUSION
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Questions?
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Image Credits
“Motivation” Slide: Construction site:
http://www.freefoto.com/images/13/12/13_12_2_prev.jpg Combine harvester:
http://www.freefoto.com/images/07/37/07_37_65_prev.jpg Tractor-trailer:
http://www.freefoto.com/images/07/37/07_37_70_prev.jpg Disaster response (with people & cranes):
http://anitokid.blogspot.com/2008/05/china-earthquake-leaves-students.html
Earthquake: http://www.cemeteryspot.com/blog/?p=180
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Extra slides
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Vehicle Routing Problem
Transportation of passengers / distribution of goods between depots and final users
Some variants: Capacitated vehicle routing with time-windows Multi-depot vehicle routing problems Pickup and delivery / dial-a-ride
RELATED WORK
iD
j
l
k
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Multi-Robot Task Allocation
Gerkey and Matarić’s (2004) categorization of MRTA problems
RELATED WORK
ST
MT
SR
MR
IA TA
Single versus multi task robots
Instantaneous versus time-extended assignment
Single versus multi robot tasks
Our problem:
(ST-SR-TA or ST-MR-TA) + inter-task constraints+ location choice
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Set-Partitioning Formulation Example
1P
B
3D
3P
1D
2D
2P
4D
A
4P
TECHNICAL APPROACH
Possible route
r3
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Set-Partitioning Formulation Example
1P
B
3D
3P
1D
2D
2P
4D
A
4P
TECHNICAL APPROACH
Possible route
r4
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Set-Partitioning Formulation Example
1P
B
3D
3P
1D
2D
2P
4D
A
4P
TECHNICAL APPROACH
Possible route
r5
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Example Problem:Feasible Route
2
3.1
4.1
B
C
A
1
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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Example Problem:Feasible Route
2
3.1
4.1
B
C
A
1
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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Example Problem:Feasible Route
1
2
3.1
4.1
B
C
A
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem:Feasible Route
1
2
3.1
4.1
B
C
A
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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INTRODUCTION RELATED WORK PROBLEM STATEMENT TECHNICAL APPROACH WORK TO DATE CONCLUSION
Example Problem:Feasible Route
1
2
3.1
4.1
B
C
A
3.2
4.2
Silo 1
Silo 2
5
3.2
4.2 5
6
6D
TECHNICAL APPROACH
Field 1
Field 2
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Dual Variables
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Details of Pricing Sub-problem