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Cooperative Algorithms for Mobile Robots and a Sensor Network

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Cooperative Algorithms for Mobile Robots and a Sensor Network. by. Maxim Batalin ... Maxim A. Batalin. Robotic Embedded Systems Laboratory. Center for Robotics ... – PowerPoint PPT presentation

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Title: Cooperative Algorithms for Mobile Robots and a Sensor Network


1
Cooperative Algorithms for Mobile Robots and a
Sensor Network
?
by Maxim Batalin
?
T2
Supervisor (USC) Gaurav Sukhatme
2
Contents
  • Introduction
  • Problems and solutions with Mobile Robots (MR)
    and Sensor Networks (SN)
  • Coverage and Exploration through Sensor Network
    Deployment
  • Sensor Network Repair and Maintenance Implicit
  • Probabilistic Navigation
  • Task Allocation Problem (scheduling)
  • DINTA
  • DINTA-MF
  • Sensor Network Repair and Maintenance - Explicit
  • Current Work Task Allocation for NIMS
  • Summary

3
Introduction
4
Why use both Mobile Robots and Sensor Networks?
  • Sensor Network provides distributed
  • Sensing
  • Computation
  • Communication
  • Ubiquitous computing is an active area of
    research and investment, hence can utilize
    intelligence which will be present in an
    environment in the future
  • At the same time Mobile robots deploy, repair and
    maintain the network, while accomplishing tasks
    that require mobility

Robots can be simpler, cheaper and dont need as
many Can solve wider variety of problems
5
Overall objectives
  • We are motivated by the idea that MRs and SN each
    could leverage strengths from the other
  • Goal Build a collaborative symbiotic system in
    which mobile robots and a sensor network solve
    tasks cooperatively and coexist benefiting each
    other
  • Assumptions
  • Global information is not accessible (no GPS, no
    map, etc.)
  • Neither robot localization nor mapping is
    performed hence robots can be simple
  • Environment can be dynamically changing
  • Assume that a collection of nodes large enough
  • Do not consider power constraints

6
Validation Platform
  • Pioneer 2DX
  • Laser (for obstacle avoidance)
  • Compass
  • Wireless ethernet
  • PC-104 stack (Pentium 1Ghz)
  • Motes
  • Atmel ATmega 128L
  • Program Flash Memory 128K bytes
  • Measurement (Serial) Flash 512K bytes
  • Configuration EEPROM 4 K bytes
  • Serial Communications UART
  • 916 MHz Multi-Channel Radio Transceiver
  • Player/Stage engine

7
Problems and solutions with Mobile Robots and
Sensor Networks
8
Coverage and Exploration
  • Deploy a group of robots maximizing sensor
    coverage
  • Cover/Explore every point of environment with an
    onboard sensor
  • Cant tell if coverage is complete
  • Cant recover robots
  • Require a LOT of expensive robots
  • Use Sensor Network

9
Approach
  • Robot Loop
  • If no beacon within radio range
  • deploy beacon
  • Else
  • move in direction suggested by nearest beacon
  • Beacon Loop
  • Emit least recently visited direction

M. Batalin, G. S. Sukhatme, Coverage, Exploration
and Deployment by a Mobile Robot and
Communication Network, Telecommunications
Systems, April 2004 (accepted, to appear) M.
Batalin, G. S. Sukhatme, Efficient Exploration
Without LocalizationProceedings of the 2003 IEEE
International Conference on Robotics and
Automation (ICRA'03), Taipei, Taiwan, May 12 -
17, 2003.
10
Simulation Experiment
11
Sensor Network Repair and Maintenance - Implicit
12
Benefits
  • A static sensor network is deployed
  • can be used for numerous network applications
  • guarantees that every point of environment would
    be eventually covered by the mobile robots
  • etc.
  • Robots can
  • be used for exploration, patrolling and coverage
    tasks
  • restore the static sensor network in case of
    damage
  • retrieve robots
  • system knows when full coverage is achieved

13
Summary
  • Theoretical analysis shows that trade offs in the
    assumptions affect the outcome significantly
  • RW robot does not have or assume anything
    RW(G) ?(VlogV )
  • Proposed Approach the number of nodes is
    enough ?(V ) and o(VlogV)
  • DFS nodes of three colors, perfect localization
    and navigation DFS(G) T(V E)
  • Proposed algorithm outperforms other algorithms
    if localization and perfect navigation cannot be
    assumed

14
Probabilistic Navigation
15
Introduction
  • How to navigate from point A to point B
  • A fundamental problem in robotics
  • No a priori information about the environment
  • Use Sensor Network

16
Transition Probabilities
  • While robot deploys and traverses sensor network
    from node to node it can determine transition
    probabilities
  • Robot stores determined probabilities on
    appropriate nodes

17
Navigation Field computation
  • Suppose transition probabilities are determined
    for all nodes
  • If a goal node is specified, the information is
    propagated through the network and the
    navigation field is computed using
  • Where - V is the utility value, C(s, a) is the
    cost associated with an action, P(ss, a) is the
    transition probability of arriving to node s if
    an action a was taken at node s, p(s) is the
    policy, or direction that the node s is going to
    suggest for the robot in the vicinity.

18
Distributed Computation
  • Suppose the SN is flooded with goal node data
  • Every node updates own utilities according to
    utility equation
  • After the utilities are computed, every node
    computes an optimal policy for itself according
    to policy equation
  • Note that the action policy computation is done
    only once, and does not need to be recomputed,
    unless the goal changes
  • Note that if neighbors of all nodes are known
    exactly the system converges after a single
    iteration.

19
Basic algorithm
  • Assume that SN deployed and Navigation Field is
    computed
  • Closest Node suggests direction of motion
  • Robot moves straight in a suggested direction
  • Determine if close to the next node based on
    signal strength
  • If no, repeat 2
  • If yes, start 1

M. Batalin, G. S. Sukhatme, Coverage, Exploration
and Deployment by a Mobile Robot and
Communication Network, Telecommunications
Systems, April 2004 (accepted, to appear)
20
Simulation Experiment
Node-to-node navigation Move in suggested
direction, switch to closest node, repeat
21
Probabilistic Navigation in Real Environment
My Cube
22
Real-World Navigation Challenges
  • Cubicle environment is narrow, hence precision
    is required
  • Compass or IMU proved to be useless inside
  • Implementation should be simple enough for simple
    robot
  • Algorithm should be based purely on signal
    strength
  • Different antennas, not truly omnidirectional
  • Ambient noise in the environment not constant
    with time
  • Hence raw signal strength thresholding or an
    observation model would not work reliably

23
Basic algorithm
  • Assume that SN deployed and Navigation Field is
    computed
  • Robot knows its initial heading and closest node
  • Closest Node suggests direction of motion
  • Robot moves in a suggested direction
  • Vector Field Histogram (VFH) algorithm is used
    for local navigation and obstacle avoidance
  • Drives the robot from A to B avoiding obstacles
    on the path on the local scale (
  • Author J. Borenstein, available in Player/Stage
  • Determine if close to the next node (next slide)
  • If no, repeat 2
  • If yes, start 1

M. Batalin, G. S. Sukhatme,M. Hattig, "Mobile
Robot Navigation using a Sensor Network, To
appear IEEE International Conference on Robotics
and Automation, 2004
24
When to switch between the nodes?
  • Similar to general state estimation problem
  • Difficult problem in general
  • Proposed algorithm which estimates when the robot
    is close to a node
  • Compute initial maximum average of the first i
    samples
  • Compute a running average which is an average of
    j consecutive samples where j
  • Threshold on ratio of averages

25
Experiments The Environment
My Cube
3
1
2
5
4
6
8
7
9
26
Experiments Run 1
1
9
27
Summary
  • Algorithm allows the robot to navigate precisely
    and reliably using a deployed sensor network.
  • Approach differs from systems described in the
    literature by assuming that the map,
    localization, GPS, IMU and compass are not
    available
  • The navigation occurs through node-wise motion
    from node to node on the path from starting node
    to the goal node
  • We conducted 50 experiments for 5 different
    goals, totaling in over 1 km of traveled
    distance
  • In each of the 50 cases the robot successfully
    navigated to the goal node
  • Note that we considered an experiment to be
    successful if the robot approached the goal node
    to within 3m

28
Task Allocation Problem
29
Introduction
  • Task Allocation (TA) is the problem of assigning
    resources (robots for example) to tasks
  • Offline TA is the problem of assigning resources
    to different tasks (processes) if the tasks
    arrival distribution is known a priori
  • Task assignments are computed offline
  • Resembles Traveling Salesperson Problem, it is
    NP-Complete
  • Online TA is the problem of assigning resources
    to tasks if the distribution of the tasks
    arrival is NOT known a priori
  • The task assignment occurs in decision epochs.
  • A decision epoch is a period of time during which
    only arrived tasks are considered for assignment.
  • It is shown in literature that in case of Online
    TA the optimal solution assigns the tasks in a
    greedy fashion
  • We consider an Online TA
  • Note that many real life TA problems are Online

30
Experimental Scenario
  • We study a particular experimental scenario -
    emergency handling
  • Alarms are detected by nodes in the static
    network
  • The problem is to assign and navigate robots to
    different alarms
  • The goal is to minimize the cumulative alarm
    OnTime across all alarms, over the duration of
    the entire experiment
  • Alarms OnTime is a difference between the time
    the alarm was turned off by a robot and the time
    the alarm was detected by one of the nodes of the
    network

31
Distributed In-Network Task Allocation (implicit)
  • Suppose sensor network monitors the environment
  • If an event is detected by node n it sends out a
    packet (n_id, weight, hop_count)
  • Compute Navigation Field
  • This computation results in a direction which
    maximizes the net utility of the robot
  • If there are several events detected at the same
    time, a node computes direction towards the goal
    node with largest

32
Multi Field Distributed In-Network Task
Allocation (explicit)
  • If an event is detected by node n it sends out a
    packet (n_id, time_of_event, weight, hop_count)
  • Every node considers task assignment in decision
    epochs
  • At the end of current decision epoch, network
    synchronizes current positions of available
    robots
  • Since every node receives the same data the
    node states are in synch an optimal greedy
    assignment is possible.
  • Hence, every node maintains a suggested direction
    per task

33
Experimental results
  • Player/Stage simulations
  • Sensor Network of 25 motes
  • Groups of 1-4 robots, 10 trials/group
  • 10 Alarms are drawn from the Poisson distribution
    with ?1/60
  • Empty environment, A 576 m2

34
Sensor Network Repair and Maintenance - Explicit
35
Benefits of DINTA DINTA-MF compared to other
techniques
  • Sensing, computation and communication are
    distributed
  • Provides sensor that is everywhere at the same
    time
  • Can estimate utilities directly
  • Does not rely on global information (no map, GPS,
    localization, etc.)
  • Can be combined with other techniques to increase
    range of applications

M. Batalin, G. S. Sukhatme, Using a Sensor
Network for Distributed Multi-Robot Task
Allocation, To appearIEEE International
Conference on Robotics and Automation, 2004 M.
Batalin, G. S. Sukhatme, "Sensor Network-based
Multi-Robot Task Allocation," In IEEE/RSJ
International Conference on Intelligent Robots
and Systems, pp. 1939-1944, 2003
36
Current Work Task Allocation for NIMS
37
47 m
Solar Cell
91
NIMS Node
84
Power Distribution Cable
Imager With Pan/Tilt Actuator
50 m
Vertical Node (Ta, RH, PAR)
Battery Pack
38
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39
Problem Definition
  • Assume a NIMS system (s1, s2, s3, ) and a Sensor
    Network deployed in the same area
  • Suppose nodes of the sensor network detect
    phenomena (p1, p2, p3, ) that require further
    study by a NIMS system
  • Compute an optimal assignment of (s1, s2, s3,
    ) (p1, p2, p3, )
  • The problem is an instance of an Online Task
    Allocation problem

40
Current Status
  • Implemented a version of the algorithm in
    simulation
  • Porting to the lab NIMS system a model of an
    actual node
  • In march plan experiments in James Reserve

41
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42
Summary
  • Coverage and Exploration through Sensor Network
    Deployment
  • Sensor Network Repair and Maintenance Implicit
    and Explicit
  • Probabilistic Navigation
  • Task Allocation Problem (scheduling)
  • DINTA
  • DINTA-MF
  • Current Work Task Allocation for NIMS

Symbiotic systems are beneficial and important to
study
43
Contacts
  • Maxim A. Batalin
  • Robotic Embedded Systems Laboratory
  • Center for Robotics and Embedded Systems
  • Computer Science Department
  • University of Southern California
  • Los Angeles, CA 90089, USA
  • maxim_at_robotics.usc.edu
  • http//www-robotics.usc.edu/maxim
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