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Heterogeneous Unmanned Networked Teams
George J. Pappas
School of Engineering and Applied Sciences
University of Pennsylvania
UXV Proliferation
UXV Proliferation
Heterogeneous Unmanned Networked Teams
Heterogeneous teams of UXVs must monitor and protect large and complex areas continuously
Search for threats, identify them, track them, neutralize them
Allocate and re-allocate tasks to different agents depending on sensor modalities, physical capabilities.
Persist in the presence of communication, sensor, mission constraints
Must dynamically collaborate with humans
The HUNT Mission
HUNT will push the state-of-the-art in complex, time-critical mission planning and execution for large numbers of heterogeneous vehicles collaborating with humans
Prior DoD efforts – ONR IA
Heterogeneous coverage with spatio-temporal constraints
Prior DoD efforts – ONR IA
Heterogeneous coverage with spatio-temporal constraints
Prior DoD efforts – DARPA HURT
Heterogeneous coverage with spatio-temporal specification
Prior DoD efforts – DARPA HURT
Heterogeneous coverage with spatio-temporal specification
Fundamental Challenges
Problems become quickly intractable - approaches are not inherently scalable to more than 4-5 agents
Coordination approaches do not explicitly incorporate differentiated roles based on individual UXV characteristics
Performance guarantees of safety, convergence, (sub)optimality, are very hard to establish
The HUNT Philosophy
HUNT will address these fundamental challenges by taking abroader interdisciplinary perspective to solve them:
1. (HUNTmates) Interdisciplinary team of researchers covering artificial intelligence, UXV control and robotics
2. (BioThinkTank) Biology, political science, cognitive psychology may offer solution templates in similar hard problems found in nature
HUNTmates
HUNTmates
Interdisciplinary team of pioneering researchers
Represent expertise in various forms of UXVs
Historical commitment to collaborative research
Historical commitment to bio-inspired approaches
Strong emphasis on formal approaches with guarantees
Impressive record of DoD transition activities
BioThinkTank
Daron Acemoglu, M.I.T.
Political economy
Coalition formation
Harvey Rubin, Penn
Genetic & metabolic networks,
persistence, tuberculosis.
David White, Penn
Animal behavior & communication
Evolutionary psychology.
John Vucetich, Michigan Tech
Population biology,
Wolf predatory behavior.
Simon Levin, Princeton
Mathematical biology
Evolutionary biology
David Skelly, Yale
Animal patterns in
Amphibious animals
Eric Horvitz, Microsoft
Cognitive computing
Human-automation
Julia Parrish, Washington
Cooperation in
Marine animals
BioThinkTank
Act as consultants for HUNTmates
Have expertise in traditionally separated domains
Are funded to visit all institutions and project meetings
As project evolves, consultants may be added or subtracted
Are NOT responsible for any project deliverables
HUNT Technical Approach
Task 1: Cataloging, modeling, and analysis of biological behaviors
Task 2: Biologically-inspired heterogeneous cooperation
Task 3: Cooperative behaviors in communication-degraded environments
Task 4: Distributed versus centralized optimization for networked control
Task 5: Embedded humans for mixed-initiative control
Task 6: Experimentation and validation
Task 1: Cataloging, modeling, and analysis of biological behaviors
Key Challenges:
Cataloging group behaviors in biology involving cross-species cooperation
Cataloging task allocation and role assignment in groups of intelligent animals
Develop mathematical models of heterogeneous and cross-species cooperation
Develop algorithms for automatic extraction of spatio-temporal behaviors
Personnel: Stephen Pratt (Lead) BioThinkTank , Vijay Kumar, Tucker Balch
Task 2: Biologically-inspired heterogeneous vehicle cooperation
Key Challenges:
Coalition formation and decision making for surveillance and coverage
Task and role assignments in heterogeneous teams
Biologically-inspired pursuit-evasion games for vehicle teams
Biologically-inspired formations of heterogeneous teams
Personnel: Ron Arkin (Lead) BioThinkTank, George Pappas, Vijay Kumar, Shankar
Sastry, Magnus Egerstedt, Dan Koditschek
Task 3: Cooperation in communication degraded environments
Key Challenges:
Cooperation over communication degraded environments
Distributed connectivity and topology control
Communication-aware motion planning and control
Personnel: Vijay Kumar (Lead) BioThinkTank, Ali Jadbabaie, Karl Hedrick, George
Pappas.
Task 4: Distributed versus centralized optimization for networked control
Key Challenges:
Optimization-based control for spatio-temporal specifications
Optimization based control for heterogeneous UXVs
Dual decomposition techniques for distributing optimization problems
Personnel: Ali Jadbabaie (Lead) BioThinkTank, Claire Tomlin, Vijay Kumar, Shankar Sastry,
George Pappas.
Task 5: Embedded humans for mixed-initiative systems
Key Challenges:
Stochastic hybrid systems control mixed-initiative systems
Compositionality of behaviors for responsiveness and robustness
Natural languages for mission specification
Personnel: Claire Tomlin (Lead) BioThinkTank, Vijay Kumar, Ron Arkin, George
Pappas.
Task 6: Experimentation and Validation
Key Challenges:
HUNT simulation for heterogeneous platform integration
Development of HUNT experimental testbeds
Individual and integrated experimentation
Personnel: Karl Hedrick (Lead) BioThinkTank, Vijay Kumar, Ron Arkin, Claire
Tomlin, George Pappas.
Project Schedule and Deliverables
Year 1 deliverables are all working papers
Year 2 deliverables also include algorithms and individual simulation experiments
Year 3 deliverables also include individual Tasks 5 and Task 6 experiments
Year 4 deliverables include an integrated experiment
Year 5 deliverables include an integrated demonstration
Institute Leads
Task Leads
TASK 1 LEAD TASK 2 LEAD TASK 3 LEAD TASK 4 LEAD TASK 5 LEAD TASK 6 LEAD
OVERALL LEAD
Synergetic ActivitiesLong record of interdisciplinary workshop organization, collaborative
research, integrated experiments, joint workshop organization, and joint publications.
Head start: ICRA 2008 Workshop on Cooperative Control of Multiple Heterogeneous UAVs for Coverage and Surveillance, Pasadena, CA.
Special Issue in IEEE Robotics and Automation Magazine (Deadline: October 15, 2008)
We will organize two high profile, community building workshops (one in base period, one in option period) with edited volumes as deliverables
Visiting students across institutions and cross-institute Ph.D. Committee supervision
DoD Transition
Impressive team record of industrial and DoD transition Berkeley UUVs transitioning to USNA and NUWC ONR AINS formation flying to ACR ONR STTR Penn/Lockheed contributes to ONR IA project
HUNT briefing to CNO Strategic Studies Group (Nov. 17)
HUNT may impact DARPA DSO FunBio program
HUNT research may impact ARL MAST Autonomy Project
Project Website
http://www.seas.upenn.edu/hunt/
Some solutions
Heterogeneous networks in complex environments
Mission specification languages
Heterogeneous Networks: MotivationConnected coverage requirement in mobile robotics applications (surveillance, coverage)
Problem: Deploy a network of robots so that communication is established between different locations
in complex environments.
Heterogeneous Networks: MotivationConnected coverage requirement in mobile robotics applications (surveillance, coverage)
Problem: Deploy a network of robots so that communication is established between different locations
in complex environments.
Heterogeneous Networks
Problem: Deploy a network of robots so that communication is established between different locations
in complex environments.
Heterogeneous Networks
Problem: Deploy a network of robots so that communication is established between different locations
in complex environments.
ConnectivityControl
LocationAssignment
Heterogeneous Networks
Problem: Deploy a network of robots so that communication is established between different locations
in complex environments.
ConnectivityControl
LocationAssignment
Leaders Relays
Heterogeneous Network
Challenges
Distributed Connectivity Control - Local estimates of the network topology
- Auction-based link deletion
Role Assignment in a Heterogeneous Team - Auction-based leader election / reelection (for leader failures)
- Leaders are assigned locations of interest
- Relays assist leaders in completing their tasks
Complex Environments - Environment interference on signal strength
- Geodesic paths
GRASP Lab floor
Target Assignment: Problem Definition
Problem: Given a group of robots and no a prioriassignment information, design distributed control lawsso that distinct agents are assigned to distinct targets.
Target Assignment: Problem Definition
Problem: Given a group of robots and no a prioriassignment information, design distributed control lawsso that distinct agents are assigned to distinct targets.
Our Approach
Desired Properties
Dynamically determinean assignment during navigationDistributed (local information)
Scalable (polynomial complexity)
From single-destination navigation functions…
(E. Rimon & D. Koditschek, 1992)
to multi-destination potential fields…
Discrete coordinationprotocols to ensureliveness & safety
Multi-Destination Potential Fields
Theorem: is free of local minima, other than the destinations.
Proof sketch: is harmonic
Coordinates of agent i and destination k, respectively.
Multi-Destination Potential:
Estimate of the available destinations:
Discrete Coordination ProtocolsAssumptions
Every robot knows the position of all destinations.Limited sensing/communication range R
Distributed CoordinationStep 1: “Select” an available target from a set .Step 2: Visit target and if it is free establish an assignment.Step 3: Update an estimate of taken and available targets (index sets) and
Discrete Coordination ProtocolsAssumptions
Every robot knows the position of all destinations.Limited sensing/communication range R
Distributed CoordinationStep 1: “Select” an available target from a set .Step 2: Visit target and if it is free establish an assignment.Step 3: Update an estimate of taken and available targets (index sets) and
Coordination via Market-Based Control
Market-Based CoordinationStep 1: Update an estimate of taken and available targets (index sets):
and
Step 2: Select an available target and an associated bid.Step 3: Among all neighbors bidding for the same target, the highest bid wins.
ChallengeNegotiate destinations before physically exploring them.
No specific destination to negotiate
Select a destination in a (eg. the closest one)
Subject to market-based negotiation
The Hybrid Agent
Assignment(Market-Based Coordination)
* Estimate available targets* Bid for an available target
* Among all neighbors bidding for the same target, the highest bid wins
Navigation
if target k is updated
positions, targets and bids from all neighbors
updated target and bid
current target k
Distributed: Only nearest neighbor information used
Scalable: At most O(n2) assignments explored before convergence
Provably Correct: Convergence to an assignment is guaranteed
Theorems & Results
Theorems & Side Results
Can handle communication limitations & time delays
Extendible to handle objectives such as collision and obstacle avoidance
Further Characteristics
M. M. Zavlanos, G. J. Pappas, IEEE T-RO, 2008.
Experimentation*
Platform: differential-drive robots (stepper motors)Tracking System: vision tracking system & robot odometry fused via Extended Kalman FilterImplementation: C++ using the open-source robotics software Player (TCP communications), part of the Player/Stage/Gazebo projectResults: Verify integrity and correctness of the asynchronous and parallel computation as well as message passing with time delays.
Courtesy of N. Michael and V. Kumar
*N. Michael, M. M. Zavlanos, V. Kumar and G. J. Pappas, ICRA 2008
Scalability & Potential Applications
Formation Stabilization
Self Assembly (Termite mounds)
Modular Robotics
M. M. Zavlanos, G. J. Pappas, IEEE CDC, 2007.
Scalability & Potential Applications
Formation Stabilization
Self Assembly (Termite mounds)
Modular Robotics
M. M. Zavlanos, G. J. Pappas, IEEE CDC, 2007.
1
2
3
4 5
6
Connectivity & Network Topology
Graph:
Vertex Set:
Edge Set:
Algebraic Representation
Adjacency Matrix Laplacian Matrix
Lemma: with eigenvector 1. Also, if , then is connected.
Potential Fields for Connectivity
State-Dependent Network:
Time Varying Edge Set:
Potential Field:
with a projection matrix to
Control Law:
M. M. Zavlanos, G. J. Pappas, IEEE T-RO, August 2007.
Connectivity modeled as an obstacle
Local ComputationGlobal Information
A Fully Distributed Approach
Desired Properties
Addition and deletion of links in Mobile Robotic NetworksAny network structure, from very sparse to very dense
Distributed (use only nearest neighbor information)Scalable (polynomial memory and computational complexity)
Local Potential Fields
Discrete CoordinationProtocols
Maintain LinksM. Ji and M. Egerstedt (2006)
Challenge
Control Link Deletions so that network connectivity is always guaranteed.
Discrete Coordination Protocols
Link Additions do not endanger connectivity
Challenge
Control Link Deletions so that network connectivity is always guaranteed.
Discrete Coordination Protocols
Link Additions do not endanger connectivity
Control Challenges
* At most one link deletion every time instant.* Agreement on the link to be deleted.
Connectivity Violation !
Market-Based
Coordination
If is connected,then is connected.
No Violation !
Connectivity Violation !
Network Estimate:
If then the is connected.
Agreement via Market-Based
Step 2: Select a link and a bid
Step 3: Initialize a set of max-bids Initialize a vector of tokens
Step 4: While do - Collect tokens from neighbors - Keep the highest bid
Step 5: If and select control
else if select control and return to Step 1.
All bids havebeen collected
Tie in the Bids
Step 1: Compute safe set of links to be deleted, i.e., links (i,j) such that
Integrating with Mobility
ObjectiveMobility does not destroy thenetwork structure controlled
by discrete coordination
Candidate agent to add a link (i,j) with
Candidate neighbor to delete a link (i,j) with
Non-neighbor agent
Definition of Links
Maintaining Links
ensured by
The Hybrid Agent
Navigation in the Config.-Spaceif neighbors are updated
positions, network estimates and bids from all neighbors
updated network estimate and bid
updated set of neighbors
Discrete Coordination ProtocolsMarket-Based Coordination for Link DeletionsLink Additions
New Links
Existing Links provided by Neighbors
Drift (negative gradient of a potential)
Maintain existing links with neighbors & collision avoidance
Distributed: Only nearest neighbor information used
Scalable: Memory cost is O(n2) due to adjacency matrixComputational cost is O(n3) due to eigenvalue computation
Provably Correct: Connectivity always guaranteed
Theorems & Results
Theorems & Side Results
Can handle communication limitations & time delays
Can handle robust notions of connectivity & collision avoidance
Achieving secondary objectives is not guaranteed
Further Characteristics
M. M. Zavlanos, G. J. Pappas, IEEE T-RO. (accepted)
Applications: FlockingAlignment:Steer towards the average heading of flockmates
Separation:Steer to avoid crowding of flockmates
Cohesion: Steer towards the average position of flockmates
Reynolds (1987)
Theorem: If the network remains connected, then all agent headingsconverge to a common value and the distances between them are stabilized
to a configuration where the group potential energy attains a minimum.
Tanner, Jadbabaie, Pappas (2007)Separation & CohesionVelocity alignment
Applications: FlockingAlignment:Steer towards the average heading of flockmates
Separation:Steer to avoid crowding of flockmates
Cohesion: Steer towards the average position of flockmates
Reynolds (1987)
Theorem: If the network remains connected, then all agent headingsconverge to a common value and the distances between them are stabilized
to a configuration where the group potential energy attains a minimum.
Tanner, Jadbabaie, Pappas (2007)Separation & CohesionVelocity alignment
Connectivity Preserving Flocking
Theorem:If the initial network is connected,
flocking is always guaranteed.
Maintain neighbor links & SeparationVelocity alignment
Distributed Connectivity Control
M. M. Zavlanos, H. G. Tanner, A. Jadbabaie, G. J. Pappas, IEEE TAC. (submitted)
Connectivity Preserving Flocking
Theorem:If the initial network is connected,
flocking is always guaranteed.
Maintain neighbor links & SeparationVelocity alignment
Distributed Connectivity Control
M. M. Zavlanos, H. G. Tanner, A. Jadbabaie, G. J. Pappas, IEEE TAC. (submitted)
Experimentation with Scarabs
N. Michael, M. M. Zavlanos, V. Kumar and G. J. Pappas, ISER 2008.
Heterogeneous Networks in Complex Environments
Distributed Connectivity Control - Local estimates of the network topology
- Auction-based link deletion
Role Assignment in a Heterogeneous Team - Auction-based leader election / reelection (for leader failures)
- Leaders are assigned locations of interest
- Relays assist leaders in completing their tasks
Complex Environments - Environment interference on signal strength
- Geodesic paths
GRASP Lab floor
Complex environments
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3
4
Future Challenges
Heterogeneous Teams of Robots Integration of multiple-modality agents and possibly humans Robust execution in dynamic environments, abstract task specification Biologically inspired cooperation principles
Control of Robotic Networks Robotic networks in indoor environments or environments with obstacles Bid selection to maximize lifetime of the robotic network Stochasticity, i.e., failing communication links/robots Fundamental limits of distributed algorithms, i.e., amount of information required
Interdisciplinary insight from Political economics for auction designs Role allocation in ants
Some solutions
Heterogeneous networks in complex environments
Mission specification languages
Linguistic Mission Specification
Interface will depend on
1. Robot domain (mobile robots)2. Tasks (search missions)3. Environments
Interface should be formal, robot-independent, robust,
Challenge:expressivity
Challenge:executability
LANGU
AGE
ROBO
T
INTERFACE
Linguistic Mission Specification
Interface will depend on
1. Robot domain (mobile robots)2. Tasks (search missions)3. Environments
Interface should be formal, robot-independent, robust,
Challenge:expressivity
Challenge:executability
LANGU
AGE
ROBO
T
INTERFACE
Our approach uses Linear Temporal Logic
(LTL)
The setting
Kress-Gazit, Fainekos, Pappas, Sensor-based temporal logic motion planning, ICRA 2007
The setting
Kress-Gazit, Fainekos, Pappas, Sensor-based temporal logic motion planning, ICRA 2007
The setting
Kress-Gazit, Fainekos, Pappas, Sensor-based temporal logic motion planning, ICRA 2007
The setting
Kress-Gazit, Fainekos, Pappas, Sensor-based temporal logic motion planning, ICRA 2007
Mission specification“Waldo may be sitting in one of rooms 1, 3, 5 and 8. Starting in corridor 12, look for him in these rooms.
If at some point you see him, stop”
Constructing φ
We consider LTL formulas of the form:
Assumptions about
environment(another robot
or human)
Desired robot behavior
*Note that only if the assumptions are met ( is true),
the desired behavior is guaranteed ( must be true).
Example
Task: “Waldo may be sitting in one of rooms 1, 3, 5 and 8. Starting in corridor 12, look for him in these rooms. If at some point you see him, stop”
Sensor (Input) propositions: X = {sWaldo}Robot (Output) propositions: Y = {r1, r2,…, r12}
Environment Assumptions Desired behavior
Initial Conditions
Transitions
Goals
Example
Task: “Waldo may be sitting in one of rooms 1, 3, 5 and 8. Starting in corridor 12, look for him in these rooms. If at some point you see him, stop”
Sensor (Input) propositions: X = {sWaldo}Robot (Output) propositions: Y = {r1, r2,…, r12}
Environment Assumptions Desired behavior
Planning using SynthesisTask: “Waldo may be sitting in one of rooms 1, 3, 5 and 8. Starting in corridor 12, look for him in these rooms. If at some point you see him, stop”
r12r9r8
r5
r8
r9r10r5
r10
r11
r3r3 r11
r12
r9
r1r1sWaldo sWaldo
sWaldosWaldo
sWaldo
sWaldos W
aldo
sWaldo
3 4
8 7
6
5
12 109
11
1
2
Multi-robot specifications
• Naturally captured in a decentralized way
• The environment of each robot contains all other robots
Multi-robot scenario*
*Kress-Gazit et al, Valet parking without a valet, IROS 2007
Multi-robot scenario*
*Kress-Gazit et al, Valet parking without a valet, IROS 2007
DARPA’s Urban Challenge - NQE
H. Kress-Gazit and G. J. Pappas. Automatically Synthesizing a Planner and Controller for the DARPA Urban Challenge. IEEE CASE 2008
Robot moving
Robot stopping
Other vehicles
Obstacles
DARPA’s Urban Challenge - NQE
H. Kress-Gazit and G. J. Pappas. Automatically Synthesizing a Planner and Controller for the DARPA Urban Challenge. IEEE CASE 2008
Robot moving
Robot stopping
Other vehicles
Obstacles
Toolbox
Toolbox
Future directions
Multi-robot specification languages Coordination hidden or exposed to the interface?
Heterogeneous specifications Model heterogeneous constraints across sensor-robot predicates
More expressive logics/task description languages Spatial logics? “Go around the car in front of the red building”
Growing interaction with cognitive linguists and psychologists