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![Page 1: 1 Automatic deployment of robotic teams from rich specifications Calin Belta Hybrid and Networked Systems (HyNeSs) Lab Departments of Mechanical / Systems.](https://reader035.fdocuments.net/reader035/viewer/2022081603/5697bfae1a28abf838c9c843/html5/thumbnails/1.jpg)
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Automatic deployment of robotic teams from rich specifications
Calin Belta
Hybrid and Networked Systems (HyNeSs) LabDepartments of Mechanical / Systems Engineering
Boston University
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Rich, natural-language specification, e.g., “USV always avoid P6. UGVs always avoid P4. Any UGV visit P1 or P2 and then go to P3. After P3 is occupied, USV go to P7 and UGV2 start surveilling P1 and P2”
P1P3
P2
P4
P5
UGV1
UGV2
UGV4
UGV3
UAV
USV
UUV
P6
P7
Automatic synthesis of provably-correct control and communication strategies.
Problem 1: Deployment of small heterogeneous teams (<10)
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“Always obey traffic rules. Visit Road R1 and then Road R2 without crossing intersection I1. If Road R8 is ever visited, then Road R3 must never be reached. Park simultaneously in adjacent parking spaces and remain there for all future times.”
Rich, natural language specification
Robotic Urban-Like Environment (RULE)
Automatic synthesis of provably-correct control and communication strategies.
Problem 1: Deployment of small heterogeneous teams (<10)
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“Eventually cover an elliptic region with center c1, semiaxes s1 and s2, and orientation θ, then a circular region with center c2 and area a, and maintain this configuration for all future times. Always maintain a pair-wise distance Dmin < D < Dmax. Always avoid obstacles”
Problem 2: Deployment of large homogeneous swarms
Rich, natural-language specification over a small set of global features, e.g.,
Automatic synthesis of control and communication strategies.
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Motivation
“USV always avoid P6. UGVs always avoid P4. Any UGV visit P1 or P2 and then go to P3. After P3 is occupied, USV go to P7 and UGV2 start visiting P1 and P2, in this order, infinitely often.”
“Always obey traffic rules. Visit Road R1 and then Road R2 without crossing intersection I1. If Road R8 is ever visited, then Road R3 must never be reached. Park simultaneously in adjacent parking spaces and remain there for all future times.”
• Place the human operator as “high” as possible in the decision hierarchy• Formalize the human-robot interaction process
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ApproachDraw inspiration from formal analysis (verification)
“Is deadlock ever possible?”“If a request is received, make sure it is eventually granted.”
Specification
Process
“Always avoid P4. Visit P1 or P2 and then go to P3. Don’t go to P6 unless P1 was visited.”
?
( , )x f x u
• Analysis / control• Compositionality
Model
Model checking (SPIN, NuSMV)
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Outline
• Finite quotients and control of continuous systems
• Deployment for small teams
• Deployment for swarms
( , )x f x u
Always avoid P4. Visit P1 or P2 and then go to P3. Don’t go to P6 unless P1 was visited.”
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Outline
• Finite quotients and control of continuous systems
• Deployment for small teams
• Deployment for swarms
( , )x f x u
Always avoid P4. Visit P1 or P2 and then go to P3. Don’t go to P6 unless P1 was visited.”
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2 4 3
4 1
3 4 1 3
( ( ))
( (
( (( ) ( )))))
Feedback automaton
Language equivalence!
control
state
Feedback controller
region
1 2 3, ,
1 3,
1
2 3, 4 3 2, ,
23
4
4 2,
1u
2u
3u
4u5u
6u
7u
6u 8u
9u10u
11u12u
13u
14u
Feedback hybrid
automaton
Finite quotients and control of continuous systems
“Avoid the grey region for all times. Visit the blue region, then the green region, and then keep surveying the striped blue and green regions, in this order.”
“(pi2 = TRUE and pi4 = FALSE and pi3 = FALSE) should never happen. Then pi4 = TRUE and then pi1 = TRUE should happen. After that, (pi3 = TRUE and pi4 = TRUE) and then (pi1 = TRUE and pi3 = FALSE) should occur infinitely often.”
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Outline
• Finite quotients and control of continuous systems
• Deployment for small teams
• Deployment for swarms
( , )x f x u
Always avoid P4. Visit P1 or P2 and then go to P3. Don’t go to P6 unless P1 was visited.”
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Deployment of small teamsArbitrary temporal and logic statements about the reachability of regions in a partitioned environment, e.g., “Avoid the blue regions until the green and red regions are simultaneously visited. Then visit any one of the blue regions”
Provably-correct control strategies and communication protocols
Assumptions: • the specification is robot-abstract• the robots can only communicate when in adjacent regions (varying communication constraint)• communication relation is symmetric• all robots in a component of the communication graph can instantaneously communicate
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“Avoid the blue regions until the green and red regions are simultaneously visited. Then visit any one of the blue regions”
Deployment of small teams
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“Avoid the blue regions until the green and red regions are simultaneously visited. Then visit any one of the blue regions”
Deployment of small teams
? ? ? ?
???
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“Do not visit any yellow region until all are simultaneously entered, and always avoid the gray regions”
8 10 16 8 10 16 4 6 15( ) ( ) ( )p p p U p p p p p p
Assume we have three agents
Deployment of small teamsExample
Simulation - movie
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Outline
• Finite quotients and control of continuous systems
• Deployment for small teams
• Deployment for swarms
( , )x f x u
Always avoid P4. Visit P1 or P2 and then go to P3. Don’t go to P6 unless P1 was visited.”
![Page 17: 1 Automatic deployment of robotic teams from rich specifications Calin Belta Hybrid and Networked Systems (HyNeSs) Lab Departments of Mechanical / Systems.](https://reader035.fdocuments.net/reader035/viewer/2022081603/5697bfae1a28abf838c9c843/html5/thumbnails/17.jpg)
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Examples:“Eventually reach a configuration with mean ml < m < mh and covariance s1l < s1 < s1h, s2l < s2 <
s2h, θl < θ < θh and maintain it for all future times. Always maintain a pair-wise distance Dmin < D < Dmax (inter-robot collision avoidance, maintaining the connectivity of the communication graph, coverage). Always avoid obstacles”
Automatic synthesis of provably-correct control and communication strategies.
Communication graph
Sensing range
Deployment for swarms
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Examples:“Eventually reach a configuration with mean ml < m < mh and covariance s1l < s1 < s1h, s2l < s2 <
s2h, θl < θ < θh and maintain it for all future times. Always maintain a pair-wise distance Dmin < D < Dmax (inter-robot collision avoidance, maintaining the connectivity of the communication graph, coverage). Always avoid obstacles”
Automatic synthesis of provably-correct control and communication strategies.
m θ
s1s2
Deployment for swarms
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Approach
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Approach
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Q
A
q
a
Approach
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0)( xr
0),( axpx W
: ( , ) 0 ( ) 0x p x a r x
Q
A
Collision avoidance:
Quantifier elimination
( ) 0s a
( ) 0s a
( ) 0s a ( ) 0s a
always always
Approach
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1( ) 0r x
0),( axpx W
Q
A
1 2: ( , ) 0 ( ) 0 ( ) 0x p x a r x r x always
2 ( ) 0r x
3: ( , ) 0 ( ) 0x p x a r x eventually
and then 4: ( , ) 0 ( ) 0x p x a r x and
3( ) 0r x 4 ( ) 0r x
Approach
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24x WQ
A
1 4 2 4,
1 3,
1 3,
3 4, 2 3 4, ,
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Approach
1 2: ( , ) 0 ( ) 0 ( ) 0x p x a r x r x always
3: ( , ) 0 ( ) 0x p x a r x eventually
and then 4: ( , ) 0 ( ) 0x p x a r x and
)()( 543321
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Distributed communication architecture
Lower dimensional continuous description
Set of essential featuresof the swarm
Output: provably correct control laws
Continuousabstraction
Hierarchicalabstractionarchitecture
Finite dimensional discrete description
Input: Temporal logic specification over essential features
Discreteabstraction
Provably correct control law
Hierarchical abstractions
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Distributed communication architecture
Lower dimensional continuous description
Set of essential featuresof the swarm
Output: provably correct control laws
Continuousabstraction
Hierarchicalabstractionarchitecture
Finite dimensional discrete description
Input: Temporal logic specification over essential features
Discreteabstraction
Provably correct control law
Continuous abstraction
?
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Q
1( )h a q
( ) ( )a dh q u q( )q u q
( )a h q
A
h
( )dh qContinuous abstraction
ConsistencyEquivalent states remain equivalent for all times under the flow
( )q u qq
ActuationAt any point any velocity can be achieved.a A a
Detectability if and only if0q 0a
Correct aggregation = Consistency + Actuation + Detectability
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{ }W
h
A G
S
Q
Continuous abstraction
• (non – scalability)
• is invariant to robot permutations
• Control architecture independent on the choice of a world frame
•
• is left invariant
• The group G and shape S can be controlled independently
NnA dim
h
),(, sgaSGA
h ( ) ( , ) ( ) ( , ),h q g s h gq gg s g G
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Continuous abstraction: examples
group: SE(2) (mean, rotation diagonalizing the cov. matrix)shape: spectrum of cov. matrix
group: SE(2) (centroid, rotation diagonalizing the in. tensor)shape: spectrum of in. tensor
group: R2 (centroid)shape: scaling factor (sum of eig)
group: SE(3) (mean, rotation diagonalizing the in. tensor)shape: spectrum of in. tensor
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Distributed communication architecture
Lower dimensional continuous description
Set of essential featuresof the swarm
Output: provably correct control laws
Continuousabstraction
Hierarchicalabstractionarchitecture
Finite dimensional discrete description
Input: Temporal logic specification over essential features
Discreteabstraction
Provably correct control law
Hierarchical abstraction
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Fully automated framework for swarm deployment from specifications allowing for:
• Containment, obstacle avoidance, inter-robot collision avoidance, cohesion• Arbitrary tasks given in terms of LTL formulas over linear predicates over mean and variance of the swarm
E
jOiR
ir
{ }W
( )h r a ( , )a s
1
1 N
ii
rN
1
1( ) ( )
NT
i ii
s r rN
Hierarchical abstraction based on mean and variance
2, Uuur iii
32: Nh
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30 robots with control bounds [-2,2] x [-2,2] in a rectangular environment with two obstacles
Hierarchical abstraction based on mean and varianceExample:
Specification: Always stay inside environment and avoid obstacles andVisit region R1 before reaching region R2 with area greater than 4 andBefore visiting R1, make sure that the pairwise distances are all greater than 0.03
1 : [5.25, 5.75] × [0.25, 0.75]R
2 : [-3.75, -3.25] × [4.25, 4.75]R
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Acknowledgements
Funding:
National Science Foundation (CNS, IIS, CCF)Air Force Office of Sponsored Research (Computational Mathematics)Army Research Office (Mathematical Sciences Division)
Collaborators:
M. Kloetzer (Boston University)V. Kumar, G. J. Pappas (U Penn)L.C.G.J.M. Habets (CWI, Eindhoven TU)