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Dr. Shankar Sastry Chairman, Dept. of Electrical Engineering

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Based on Yamaha R-50 industrial helicopter. Berkeley BEAR Fleet: Ursa Magna2 (1999 ... Yamaha Receiver (using HW INT & proxy) Ground computer. Win 98 ... – PowerPoint PPT presentation

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Title: Dr. Shankar Sastry Chairman, Dept. of Electrical Engineering


1
Dr. Shankar Sastry Chairman, Dept. of Electrical
Engineering Computer Sciences University of
California, Berkeley ONR Summer Review, August
1, 2001
Berkeley UAV / UGV Testbed
2
UAV Design Procedure
3
Berkeley BEAR Fleet Ursa Minor3 (1999- )
Boeing DQI-NP on gel mounting
GPS Card
GPS Antenna
Wireless Modem
Length 1.4m Width 0.39m Height 0.47m Weight
9.4 kg Engine Output 2.8 bhp Rotor Diameter
1.5m Flight time 15 min System operation time
30 min
Navigation computer
Radio Receiver
4
Bergen with shock-absorbing landing gear
  • Pneumatic-operating
  • shock-absorbing
  • landing gear

Length 1.5m Width0.3m Height 0.7m Dry Weight
8 kg Payload 10kg
5
Berkeley BEAR Fleet Ursa Magna2 (1999- )
Based on Yamaha R-50 industrial helicopter
Camera
GPS Antenna
Wavelan Antenna
Ultrasonic Height meter
Integrated Nav/Comm Module
Length 3.5m Width0.7m Height 1.08m Dry Weight
44 kg Payload 20kg Engine Output 12 hp Rotor
Diameter 3.070m Flight time 60 min System
operation time 60 min
Boeing DQI-NP on fluid mounting
6
Berkeley BEAR Fleet Ursa Maxima 1 (2000- )
Based on Yamaha RMAX industrial helicopter
Integrated Nav/Comm Module
Length 3.63m Width0.72m Height 1.08m Dry
Weight 58 kg Payload 30kg Engine Output 21
hp Rotor Diameter 3.115m Flight system
operation time 60 min
7
Hierarchy of the UAVS Management System
8
Flight Control System signal flow
CONTROL CHANNEL SELECTION, TAKE-OVER DECISION
CONTROL MODE
NAV SENSOR SUITE
Flight Data
Human pilot Control input
PWM READING CTC 2,8,9,10
FEEDFORWARD/ FEEDBACK CONTROL
PWM GENERATION CTC 3,4,5,6,7
YAMAHA RECEIVER
PWM CH1-5
PWM CH1-5
PWM CH1-5
PWM CH1-5
MECHANICAL RELAY ARRAY
OPTO- ISOLATOR
PWM DRIVER
PWM CH1-5
PWM CH1-5
PWM CH1-5
Full manual mode
YACS (YAMAHA ATTITUDE CONTROL SYSTEM)
SERVO x5
PWM CH1-5
9
Navigation Hardware Ursa Maxima 1
NOVATELGPS RT-2
SECONDARY NAV COMP Win98 PC104 K6-400
Digital Compass
ROUTER FreeBSD MediaGX233
BATT
BATT
BATT
BATT
BOEING DQI-NP
DC/DC Converter (for DQI-NP24V)
NAV COMP QNX PC104 K6-400
Lucent WaveLAN
Ethernet Hub
Constructed by Hoam Chung, David Shim, September
2000
10
Navigation Software DQI-NP-Based
ULREAD
VCOMM
PERIODIC
APERIODIC
Processes running on QNX
DQICONT
PERIODIC
100Hz
Ground Station
DGPS measurement
PRTK_at_ 5Hz PXY_at_1Hz
DQIGPS
PERIODIC
ANYTIME
Ground computer Win 98
11
Wireless Communication
DGPS Correction Broadcast via WaveLAN or Wireless
Modem
12
UAV FCS Design Regulation Layer
13
Flight Control System Experiments
Landing scenario with SAS (Dec 1999)
PositionHeading Lock (Dec 1999)
PositionHeading Lock (May 2000)
Attitude control with mu-syn (July 2000)
14
Flight control synthesis way-point navigation
Helicopter Mode transition
Sideslip
Pirouette
Bank-to-turn
Take-off
Hover
Forward Flight
Ascend/ Descend
Land
15
Vehicle Control Language
  • Objective
  • Develop an abstract and unified UAV mission
    control language environment
  • Features
  • Mission-independent
  • Executed as batch or interactive mode
  • Seamlessly integrated with existing hierarchy
  • Can be integrated with graphic interface via
    automatic code generator

16
Waypoint Navigation using VCL (Jan 29, 2001)
17
Vision Based Motion Estimation for UAV Landing
  • Cory Sharp, Omid Shakernia
  • Department of EECS
  • University of California at Berkeley

18
Vision in Control Loop
Camera
Pan/Tilt Control
Helicopter State
Control Strategy Vehicle Control Language
Navigation Computer
Vision Computer
19
Vision System Hardware
  • Ampro embedded PC Little Board P5/x
  • Low power Pentium 233MHz, running LINUX
  • 440 MB flashdisk HD, robust to body vibration
  • Runs motion estimation algorithm
  • Controls PTZ camera
  • Motion estimation algorithms
  • Written and optimized in C using LAPACK
  • Give motion estimates at 30 Hz

20
Vision System Software
21
Vision Ground Station
22
Pitching Landing Deck
  • Landing deck to simulate motion of a ship at sea
  • 6 electrically actuated cylindrical shafts
  • Motion Parameters
  • sea state (freq, amp of waves)
  • ship speed
  • direction into waves
  • Stiffened Aluminum construction
  • Dimensions 8 x 6

23
Hovering Above Deck
24
Landing on Deck
25
Probabilistic Pursuit-Evasion Games with UGVs and
UAVs
  • J. Kim, D. Shim, R. Vidal, O. Shakernia,
  • C. Sharp, S. Rashid, S. Sastry
  • University of California at Berkeley
  • 04/05/01

26
Outline
  • Introduction
  • Pursuit Evasion Games
  • Map Building
  • Pursuit Policies
  • Hierarchical Control Architecture
  • Strategic Planner, Tactical Planner, Regulation,
    Sensing, Control
  • System, Agent and Communication Architectures
  • Architecture Implementation
  • Tactical Layer UGVs, UAVs, Hardware, Software,
    Sensor Fusion
  • Strategic Layer Map Building, Pursuit Policies,
    Visual Interface
  • Experimental Results
  • Evaluation of Pursuit Policies
  • Pursuit Evasion Games with UGVs and UAVs
  • Conclusions and Current Research

27
Key Ideas
  • The mission-level control of Unmanned Air
    Vehicles requires a probabilistic framework to
    analyze decision-making in the face of
    uncertainty.
  • The problem of coordinating teams of autonomous
    agents in conflict situations is naturally
    formulated in a game theoretical setting.
  • Exact solutions for these types of problems are
    often computationally intractable and, in some
    cases, open research problems.

28
The rules of the game
obstacles
UAVs
evader
29
The rules of the game
  • Terrain
  • with fixed/moving obstacles
  • not accurately mapped
  • Pursuers capable of
  • flying between obstacles
  • seeing a region around them (limited by the
    occlusions)
  • Evader(s) capable of
  • moving between obstacles (possibly actively
    avoiding detection)

Objective find the evader in minimum time
30
Scenarios
obstacles
  • search and rescue operations

UAVs
person in danger
31
Scenarios
obstacles
  • search and rescue operations
  • finding parts in a warehouse

part
32
Scenarios
obstacles
  • search and rescue operations
  • finding parts in a warehouse
  • search and capture operations

UCAVs
enemy
33
Scenarios
obstacles
  • search and rescue operations
  • finding parts in a warehouse
  • search and capture operations
  • monitoring environmental threats

UCAVs
fire
34
Cooperative Observation Problem Difficulty
obstacles
  • Suppose at each instant in time, the location of
    all evaders is given.
  • Optimal placement of pursuers in order that the
    max of evaders are visible?
  • - NP-hard
  • Reducible to Vertex Cover Problem with G(V,E)

pursuer
evader
35
Strategies for pursuit-evasion games
LaValle et al. considered a similar problem but
assume the map of the region is known, the
pursuers have perfect sensors, and worst case
trajectories for the evader
How many UAVs are needed to win the game in
finite time?
1 agent is sufficient
2 agents are needed (no matter what strategy a
single pursuer chooses, there is a trajectory for
the evader that avoids detection)
36
Cooperative map building
  • Frontier boundary between known and unknown
    regions in a map
  • Yamauchi et al. identify all the frontier
    regions in the current map and then drive to the
    nearest
  • Simmons et al. choose the frontier which will
    provide the highest utility (expected of cells
    the robot can see from the frontier-distance from
    the robot )
  • Multiple robots are coordinated to reduce overlap
    while exploring

37
Exploring a region to build a map
Deng, Papadimitriou, et al., study the problem of
building a map (seeing all points in the region)
traversing the smallest possible distance.
standard keep wall to the right algorithm
algorithm that takes better advantage of the
sensing capabilities
38
A two step solution…
  • exploration followed by pursuit is not efficient
  • sensors are imprecise
  • worst case assumptions on the trajectories of the
    evaders leads to very conservative results

exploration
pursuit
39
A different approach…
Use a probabilistic framework to combine
exploration and pursuit-evasion games.
exploration
  • Non determinism comes from
  • poorly mapped terrain
  • noise and uncertainty in the sensors
  • probabilistic models for the motion of the evader
    and the UAVs

pursuit
40
Introduction The Pursuit-Evasion Scenario
Evade!
41
Problem Formulation

42
Optimal Pursuit Policy
  • Performance measure capture time
  • Optimal policy m minimizes the cost

43
Optimal Pursuit Policy
  • cost-to-go for policy m, when the pursuers start
    with Yt Y and a conditional distribution p for
    the state x(t)
  • cost of policy m

44
Persistent pursuit policies
  • Optimization using dynamic programming is
    computationally intensive.
  • Persistent pursuit policy g

45
Persistent pursuit policies
  • Persistent pursuit policy g with a period T

46
Pursuit Policies
  • Greedy Policy
  • Pursuer moves to the cell with the highest
    probability of having an evader at the next
    instant
  • Strategic planner assigns more importance to
    local or immediate considerations
  • u(v) list of cells that are reachable from the
    current pursuers position v in a single time step.

47
Persistent Pursuit Policies for unconstrained
motion
  • Theorem 1 (Hespanha, Kim, Sastry)
  • For unconstrained motion
  • The greedy policy is persistent.
  • -The probability of the capture time being
    finite is equal to one
  • -The expected value of the capture time is
    finite

48
Persistent Pursuit Policies for constrained motion
  • Assumptions
  • For any
  • Theorem 2 (HKS00)
  • For constrained motion
  • There is an admissible pursuit policy that is
    persistent on the average with period

49
Persistent Pursuit Policies for constrained motion
  • Assumptions
  • For any
  • Theorem 2, for constrained motion
  • There is an admissible pursuit policy that is
    persistent on the average with period

50
Pursuit Policies
  • Global-Max Policy
  • Pursuer moves towards the place with the highest
    probability of having an evader in the map
  • May not take advantage of multiple pursuers (may
    move to the same place if not properly
    coordinated)

51
Introduction Theoretical Issues
  • Probabilistic map building
  • Coordinated multi-agent operation
  • Networking and intelligent data sharing
  • Path planning
  • Identification of vehicle dynamics and control
  • Sensor integration
  • Vision system

52
Pursuit-Evasion Games
  • Consider approach in Hespanha, Kim and Sastry
  • Multiple pursuers catching one single evader
  • Pursuers can only move to adjacent empty cells
  • Pursuers have perfect knowledge of current
    location
  • Sensor model false positives (p) and negatives
    (q) for evader detection
  • Evader moves randomly to adjacent cells
  • Extensions in Rashid and Kim
  • Multiple evaders each one is recognized
    individually
  • Supervisory agents can fly over obstacles and
    evaders, cannot capture
  • Sensor model for obstacle detection as well

53
Map Building Map of Obstacles
  • Sensor model
  • p prob of false positive
  • q prob of false negative
  • For a map, M, If sensor makes positive reading
  • M (x,y,t) (1-q)M(x,y,t-1)/((1-q)M(x,y,t-1)p(
    1-M(x,y,t))
  • If sensor make negative reading
  • M (x,y,t) qM(x,y,t-1)/(qM(x,y,t-1)(1-p)(1-M(
    x,y,t))

54
Map Building Map of Evaders
At each t,
55
Pursuit Policies
  • Theorem 1 (Hespanha, Kim, Sastry)
  • For a greedy policy,
  • The probability of the capture time being finite
    is equal to one
  • The expected value of the capture time is finite
  • Theorem 2 (Hespanha, Kim, Sastry)
  • For a stay-in-place policy,
  • The expected capture time increases as the speed
    of the evader decreases
  • If the speed of the evader is zero, then the
    probability of the capture time being finite is
    less than one.

56
Hierarchical System Architecture
communications network
tactical planner regulation
57
Pursuit-Evasion Game Experimental Setup
  • Experiment Setup
  • -Cooperation of
  • -One Aerial Pursuer (Ursa Magna 2)
  • Three Ground Pursuer (Pioneer UGV)
  • Against One Ground Evader (Pioneer UGV)
  • (Random or Counter-intelligent Motion)
  • -Wireless Peer-to-Peer Network

Arena 20x20 meter flat surface at RFS Cell 1m x
1m Detection Vision-based or simulated
Aerial Pursuer
Vehicle Position Vision Sensor
Waypt Request
Ground Pursuer
3x3m Camera View
GroundEvader
Vehicle Position Vision Sensor
Centralized Ground Station
58
Pioneer Ground Robots
  • Hardware
  • Micro controller motion control
  • Onboard computer communication, video
    processing, camera control
  • Sensors
  • Sonars obstacle avoidance, map building
  • GPS compass positioning
  • Video camera map building, navigation, tracking
  • Communication
  • Serial
  • Wave-LAN communication between robots and base
    station
  • Radio modem GPS communication

59
PEG Information Flow in centralized structure
Pursuer UAV
Wireless Network
Ground-based Strategy Planner
Current Pos
Flight Computer
Waypt Request
Vision DATA
Vision Computer
Policy Calculator
Agent Pos Requests
Pursuer UGV
Probability Map
Vision DATA
Vision Computer
Map Builder
Current Coord of Agents Processed Vision Input
Current Pos
Motion Controller
Waypt Request
Display Info
Evader UAV
Map Builder
Vision Computer
Current Pos
Motion Controller
60
PEG Information Flow partially distributed
structure
61
Pursuit-Evasion Game Experiment Setup
Waypoint Command
Pursuer UAV
Current Position, Vehicle Stats
Evader location detected by Vision system
Ground Command Post
Current Position, Vehicle Stats
Evader UGV
62
Pursuit-Evasion Game Experiment using Simulink
  • PEG with four UGVs
  • Global-Max pursuit policy
  • Simulated camera view
  • (radius 7.5m with 50degree conic view)
  • Pursuer0.3m/s Evader0.5m/s MAX

63
Experimental Results Pursuit Evasion Games with
4UGVs (Spring 01)
64
Pursuit-Evasion Game Experiment using Simulink
  • PEG with four UGVs
  • Global-Max pursuit policy
  • Simulated camera view
  • (radius 7.5m with 50degree conic view)
  • Pursuer0.3m/s Evader0.5m/s MAX

65
Experimental Results Pursuit Evasion Games with
4UGVs and 1 UAV (Spring 01)
66
Experimental Results Evaluation of Policies for
different visibility
Capture time of greedy and glo-max for the
different region of visibility of pursuers 3
Pursuers with trapezoidal or omni-directional
view Randomly moving evader
  • Global max policy performs better than greedy,
    since the greedy policy selects movements based
    only on local considerations.
  • Both policies perform better with the trapezoidal
    view, since the camera rotates fast enough to
    compensate the narrow field of view.

67
Experimental Results Evaders Speed vs.
Intelligence
Capture time for different speeds and levels of
intelligence of the evader 3 Pursuers with
trapezoidal view global maximum policy Max
speed of pursuers 0.3 m/s
  • Having a more intelligent evader increases the
    capture time
  • Harder to capture an intelligent evader at a
    higher speed
  • The capture time of a fast random evader is
    shorter than that of a slower random evader, when
    the speed of evader is only slightly higher than
    that of pursuers.

68
Evolution of Wireless MacroMotes Kris Pister,
David Culler and gang

69
COTS Dust
  • ATMEL 4 Mhz CPU
  • RFM 916 MHz radio OOK
  • 10-100m range
  • 64KB EEPROM
  • Sensor Bus
  • 7 Analog sensors
  • 2 I2C buses
  • 1 SPI bus
  • Runs Tiny OS
  • 2 weeks on AA batteries
  • 1 duty w/ solar power (indoors)
  • Available from Crossbow (xbow.com) for 200

70
MAVs for Delivery
www.spyplanes.com
  • 60 mph
  • 18 min
  • 1 mi comm

71
Last 2 of 6 motes are dropped from MAV
72
Tiny OS (TOS)
  • Jason Hill, Robert Szewczyk, Alec Woo, David
    Culler
  • TinyOS
  • Ad hoc networking

73
Smart Dust 01 Goal
74
Overall System Architecture
Pursuer Sensor Web Network dropped by MAVs
tactical planner regulation
Evader Sensor Web Network
75
Sensor WeBS for Pursuit Evasion games
  • A distributed network of sensor motes is dropped
    by an MAV and this is used by the UAVs/UGVs to be
    able to localize and chase the pursuers.
  • Variations pursuers have access to one set of
    sensor motes and evaders have access to other
    sensor motes
  • Other variations attack of sensor webs of
    pursuer and evader during the game for deception
    and counter-intelligence.
  • Bake Off against Vision Based Pursuit Evasion
    Games
  • Mobile Macro-Motes for dynamic networking for the
    pursuer/evasion games.
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