Heterogeneous Unmanned Networked Teams. George J. Pappas School of Engineering and Applied Sciences University of Pennsylvania

Transcription

1 Heterogeneous Unmanned Networked Teams George J. Pappas School of Engineering and Applied Sciences University of Pennsylvania

2 UXV Proliferation

3 UXV Proliferation

4 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

5 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

6 Prior DoD efforts ONR IA Heterogeneous coverage with spatio-temporal constraints

7 Prior DoD efforts ONR IA Heterogeneous coverage with spatio-temporal constraints

8 Prior DoD efforts DARPA HURT Heterogeneous coverage with spatio-temporal specification

9 Prior DoD efforts DARPA HURT Heterogeneous coverage with spatio-temporal specification

10 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

11 The HUNT Philosophy HUNT will address these fundamental challenges by taking a broader 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

12 HUNTmates

13 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

14 BioThinkTank Daron Acemoglu, M.I.T. Political economy Coalition formation Simon Levin, Princeton Mathematical biology Evolutionary biology Harvey Rubin, Penn Genetic & metabolic networks, persistence, tuberculosis. David White, Penn Animal behavior & communication Evolutionary psychology. David Skelly, Yale Animal patterns in Amphibious animals Eric Horvitz, Microsoft Cognitive computing Human-automation John Vucetich, Michigan Tech Population biology, Wolf predatory behavior. Julia Parrish, Washington Cooperation in Marine animals

15 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

16 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

17 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

18 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

19 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: Pappas. Vijay Kumar (Lead) BioThinkTank, Ali Jadbabaie, Karl Hedrick, George

20 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.

21 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: Pappas. Claire Tomlin (Lead) BioThinkTank, Vijay Kumar, Ron Arkin, George

22 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.

23 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

24 Institute Leads

25 Task Leads OVERALL LEAD TASK 1 LEAD TASK 2 LEAD TASK 3 LEAD TASK 4 LEAD TASK 5 LEAD TASK 6 LEAD

26 Synergetic Activities Long 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

27 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

28 Project Website

29 Some solutions Heterogeneous networks in complex environments Mission specification languages

30 Heterogeneous Networks: Motivation Connected 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.

31 Heterogeneous Networks: Motivation Connected 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.

32 Heterogeneous Networks Problem: Deploy a network of robots so that communication is established between different locations in complex environments.

33 Heterogeneous Networks Problem: Deploy a network of robots so that communication is established between different locations in complex environments. Connectivity Control Location Assignment

34 Heterogeneous Networks Problem: Deploy a network of robots so that communication is established between different locations in complex environments. Connectivity Control Location Assignment Leaders Relays Heterogeneous Network

35 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 GRASP Lab floor - Environment interference on signal strength - Geodesic paths

36 Target Assignment: Problem Definition Problem: Given a group of robots and no a priori assignment information, design distributed control laws so that distinct agents are assigned to distinct targets.

37 Target Assignment: Problem Definition Problem: Given a group of robots and no a priori assignment information, design distributed control laws so that distinct agents are assigned to distinct targets.

38 Our Approach From single-destination navigation functions (E. Rimon & D. Koditschek, 1992) to multi-destination potential fields Desired Properties Dynamically determine an assignment during navigation Distributed (local information) Scalable (polynomial complexity) Discrete coordination protocols to ensure liveness & safety

39 Multi-Destination Potential Fields Estimate of the available destinations: Coordinates of agent i and destination k, respectively. Multi-Destination Potential: Theorem: is free of local minima, other than the destinations. Proof sketch: is harmonic

40 Discrete Coordination Protocols Assumptions Every robot knows the position of all destinations. Limited sensing/communication range R Distributed Coordination Step 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

41 Discrete Coordination Protocols Assumptions Every robot knows the position of all destinations. Limited sensing/communication range R Distributed Coordination Step 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

42 Coordination via Market-Based Control Challenge Negotiate destinations before physically exploring them. Select a destination in a (eg. the closest one) No specific destination to negotiate Subject to marketbased negotiation Market-Based Coordination Step 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.

43 The Hybrid Agent positions, targets and bids from all neighbors Assignment (Market-Based Coordination) * Estimate available targets * Bid for an available target * Among all neighbors bidding for the same target, the highest bid wins updated target and bid current target k Navigation if target k is updated

44 Theorems & Side Results Theorems & Results Distributed: Only nearest neighbor information used Scalable: At most O(n 2 ) assignments explored before convergence Provably Correct: Convergence to an assignment is guaranteed Further Characteristics Can handle communication limitations & time delays Extendible to handle objectives such as collision and obstacle avoidance M. M. Zavlanos, G. J. Pappas, IEEE T-RO, 2008.

45 Experimentation* Platform: differential-drive robots (stepper motors) Tracking System: vision tracking system & robot odometry fused via Extended Kalman Filter Implementation: C++ using the open-source robotics software Player (TCP communications), part of the Player/Stage/Gazebo project Results: 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

46 Scalability & Potential Applications Formation Stabilization Modular Robotics M. M. Zavlanos, G. J. Pappas, IEEE CDC, Self Assembly (Termite mounds)

47 Scalability & Potential Applications Formation Stabilization Modular Robotics M. M. Zavlanos, G. J. Pappas, IEEE CDC, Self Assembly (Termite mounds)

48 Connectivity & Network Topology 4 5 Graph: Vertex Set: 3 Edge Set: Adjacency Matrix Laplacian Matrix Algebraic Representation Lemma: with eigenvector 1. Also, if, then is connected.

49 Potential Fields for Connectivity State-Dependent Network: Time Varying Edge Set: Potential Field: Local Computation Global Information with a projection matrix to Connectivity modeled as an obstacle Control Law: M. M. Zavlanos, G. J. Pappas, IEEE T-RO, August 2007.

50 A Fully Distributed Approach Discrete Coordination Protocols Local Potential Fields Maintain Links M. Ji and M. Egerstedt (2006) Desired Properties Addition and deletion of links in Mobile Robotic Networks Any network structure, from very sparse to very dense Distributed (use only nearest neighbor information) Scalable (polynomial memory and computational complexity)

51 Discrete Coordination Protocols Link Additions do not endanger connectivity Challenge Control Link Deletions so that network connectivity is always guaranteed.

52 Discrete Coordination Protocols Link Additions do not endanger connectivity Challenge Control Link Deletions so that network connectivity is always guaranteed.

53 Control Challenges No Violation! Network Estimate: If then is connected, is connected. Connectivity Violation! If then the is connected. * At most one link deletion every time instant. * Agreement on the link to be deleted. Market-Based Coordination Connectivity Violation!

54 Agreement via Market-Based Step 1: Compute safe set of links to be deleted, i.e., links (i,j) such that 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 All bids have been collected else if select control and return to Step 1. Tie in the Bids

55 Integrating with Mobility Objective Mobility does not destroy the network structure controlled by discrete coordination ensured by Maintaining Links Definition of Links Non-neighbor agent Candidate neighbor to delete a link (i,j) with Candidate agent to add a link (i,j) with

56 The Hybrid Agent positions, network estimates and bids from all neighbors New Links Discrete Coordination Protocols Market-Based Coordination for Link Deletions Link Additions Existing Links provided by Neighbors updated network estimate and bid if neighbors are updated Navigation in the Config.-Space updated set of neighbors Maintain existing links with neighbors & collision avoidance Drift (negative gradient of a potential)

57 Theorems & Side Results Theorems & Results Distributed: Only nearest neighbor information used Scalable: Memory cost is O(n 2 ) due to adjacency matrix Computational cost is O(n 3 ) due to eigenvalue computation Provably Correct: Connectivity always guaranteed Further Characteristics Can handle communication limitations & time delays Can handle robust notions of connectivity & collision avoidance Achieving secondary objectives is not guaranteed M. M. Zavlanos, G. J. Pappas, IEEE T-RO. (accepted)

58 Alignment: Applications: Flocking Steer towards the average heading of flockmates Separation: Steer to avoid crowding of flockmates Reynolds (1987) Cohesion: Steer towards the average position of flockmates Velocity alignment Separation & Cohesion Tanner, Jadbabaie, Pappas (2007) Theorem: If the network remains connected, then all agent headings converge to a common value and the distances between them are stabilized to a configuration where the group potential energy attains a minimum.

59 Alignment: Applications: Flocking Steer towards the average heading of flockmates Separation: Steer to avoid crowding of flockmates Reynolds (1987) Cohesion: Steer towards the average position of flockmates Velocity alignment Separation & Cohesion Tanner, Jadbabaie, Pappas (2007) Theorem: If the network remains connected, then all agent headings converge to a common value and the distances between them are stabilized to a configuration where the group potential energy attains a minimum.

60 Connectivity Preserving Flocking Distributed Connectivity Control Velocity alignment Maintain neighbor links & Separation Theorem: If the initial network is connected, flocking is always guaranteed. M. M. Zavlanos, H. G. Tanner, A. Jadbabaie, G. J. Pappas, IEEE TAC. (submitted)

61 Connectivity Preserving Flocking Distributed Connectivity Control Velocity alignment Maintain neighbor links & Separation Theorem: If the initial network is connected, flocking is always guaranteed. M. M. Zavlanos, H. G. Tanner, A. Jadbabaie, G. J. Pappas, IEEE TAC. (submitted)

62 Experimentation with Scarabs N. Michael, M. M. Zavlanos, V. Kumar and G. J. Pappas, ISER 2008.

63 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 GRASP Lab floor - Environment interference on signal strength - Geodesic paths

64 Complex environments

65 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

66 Some solutions Heterogeneous networks in complex environments Mission specification languages

67 Linguistic Mission Specification Challenge: expressivity Challenge: executability LANGUAGE INTERFACE ROBOT Interface will depend on 1. Robot domain (mobile robots) 2. Tasks (search missions) 3. Environments Interface should be formal, robot-independent, robust,

68 Linguistic Mission Specification Challenge: expressivity Challenge: executability LANGUAGE INTERFACE ROBOT Interface will depend on Our approach uses 1. Robot domain (mobile robots) Linear Temporal Logic 2. Tasks (search missions) (LTL) 3. Environments Interface should be formal, robot-independent, robust,

69 The setting Kress-Gazit, Fainekos, Pappas, Sensor-based temporal logic motion planning, ICRA 2007

70 The setting Kress-Gazit, Fainekos, Pappas, Sensor-based temporal logic motion planning, ICRA 2007

71 The setting Kress-Gazit, Fainekos, Pappas, Sensor-based temporal logic motion planning, ICRA 2007

72 The setting Kress-Gazit, Fainekos, Pappas, Sensor-based temporal logic motion planning, ICRA 2007

73 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

74 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).

75 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 = {s Waldo } Robot (Output) propositions: Y = {r 1, r 2,, r 12 } Environment Assumptions Desired behavior

76 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 = {s Waldo } Robot (Output) propositions: Y = {r 1, r 2,, r 12 } Environment Assumptions Desired behavior Initial Conditions Transitions Goals

77 Planning using Synthesis 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 s Waldo r 5 r 8 s Waldo r 5 s Waldo s Waldo r r 10 9 r 8 r 9 r 12 r 10 r 1 s Waldo r 1 s Waldo s Waldo r 11 r 3 r 3 r 11 r 12 s Waldo r

78 Multi-robot specifications Naturally captured in a decentralized way The environment of each robot contains all other robots

79 Multi-robot scenario* *Kress-Gazit et al, Valet parking without a valet, IROS 2007

80 Multi-robot scenario* *Kress-Gazit et al, Valet parking without a valet, IROS 2007

81 DARPA s Urban Challenge - NQE Robot moving Robot stopping Other vehicles Obstacles H. Kress-Gazit and G. J. Pappas. Automatically Synthesizing a Planner and Controller for the DARPA Urban Challenge. IEEE CASE 2008

82 DARPA s Urban Challenge - NQE Robot moving Robot stopping Other vehicles Obstacles H. Kress-Gazit and G. J. Pappas. Automatically Synthesizing a Planner and Controller for the DARPA Urban Challenge. IEEE CASE 2008

83 Toolbox

84 Toolbox

85 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