ALLIANCE: An Architecture for Fault Tolerant Multirobot Cooperation

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ALLIANCE An Architecture for Fault Tolerant
Multirobot Cooperation

• L. E. Parker, 1998
• Presented by Guoshi Li
• April 25th, 2005

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Presentation Outline

• Introduction
• Background
• Alliance
• Results
• Conclusion

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Introduction

• For smaller-scale applications, the single robot
  approach is often feasible
• A large number of the human solutions to these
  real world applications of interest employ the
  use of multiple humans supporting and
  complementing each other
• The use of robot teams for automated solutions to
  some real applications is feasible and necessary

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Introduction

• Advantages to using a distributed mobile robot
  system
• Reduce the total cost of the system
• Increase the robustness of the system by taking
  advantage of the parallelism and redundancy of
  multiple robots
• Accomplish a mission which requires the use of
  multiple robots working simultaneously on
  different aspects with time constraints

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Introduction

• Challenges of the use of multiple robots
• May actually increase the complexity of an
  automated solution
• Achieving coherence
• Determining how to decompose and allocate the
  problem among a group of robots
• Determining how to enable the robots to interact

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Introduction

• Fault tolerance
• The ability of the robot team to respond to
  individual robot failures of failures in
  communication
• Adaptivity
• The ability of the robot team to change its
  behavior over time in response to a dynamic
  environment, changes in the team mission, or
  changes in the team capabilities or composition

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Background

• Cooperative mobile robotics
• Swarm-type cooperation
• Intentional cooperation
• Swarm-type cooperation
• Deals with large numbers of homogeneous robots
• Useful for nontime-critical applications
• Globally interesting behavior can emerge as a
  result of the local interactions of the robots
• A key research issue determining the proper
  design of the local control that will allow the
  collection of robots to solve a given problem

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Background

• Intentional cooperation
• Deals with a limited number of typically
  heterogeneous robots
• Has to address some sort of efficiency constraint
• Usually require that several distinct tasks be
  performed
• Key issues include robustly determining which
  robot should perform which task so as to maximize
  the efficiency of the team and ensuring the
  proper coordination among team members

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Background

• Two bodies of previous research are particular
  applicable to intentional cooperation
• Developing control algorithms and implementing
  them either on physical robots or on simulations
  of physical robots
• Noreils sense-model-plan-act control
  architecture
• Caloud et al. another sense-model-plan-act
  architecture
• Asama et al. decentralized robot system called
  ACTRESS
• Wang the use of several distributed mutual
  exclusion algorithms
• Cohen et al. hierarchical subdivision of
  authority to address the problem of cooperative
  fire-fighting

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Background

• Distributed Artificial intelligence (DAI)
• Produced a great deal of work addressing
  intentional cooperation among generic agents
• Typically software systems running as interacting
  processes to solve a common problem rather than
  embodied, sensor-based robots
• Use a distributed, negotiation-based mechanism to
  determine the allocation of tasks to agents

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Motivations for ALLIANCE

• Earlier DAI approaches typically either make no
  serious effort at achieving fault tolerant,
  adaptive control or assume the presence of
  unspecified black boxes that continually
  monitor the environment and provide recovery
  strategies
• Control architecture must explicitly address the
  dynamic nature of the cooperative team and its
  environment to be truly useful in real-world
  applications
• The earlier approaches break the problem into a
  traditional AI sense-model-plan-act decomposition
  rather than the functional decomposition used in
  behavior-based approaches
• A behavior-based approach to cooperation should
  be used to increase the robustness and adaptivity

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Assumptions of ALLIANCE

• The robots on the team can detect the effect of
  their own actions, with some probability greater
  than 0
• Robot i can detect the actions of other team
  members for which i has redundant capabilities,
  with some probability greater than 0
• Robots on the team do not lie and are not
  intentionally adversarial
• The communications medium is not guaranteed to be
  available
• The robots do not process perfect sensors and
  effectors
• Any of the robot subsystem can fail, with some
  probability greater than 0
• If a robot fails, it cannot necessarily
  communicate its failure to its teammates
• A centralized store of complete world knowledge
  is not available

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Overview of ALLIANCE

• Design goals create robot teams that are able to
  cope with failures and uncertainty in action
  selection and action execution, and with changes
  in a dynamic environment
• A fully distributed, behavior-based software
  architecture which gives all robots the
  capability to determine their own actions based
  upon their current situation
• No centralized control is utilized
• Defines a mechanism that allows teams of robots
  to individually select appropriate actions

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Overview of ALLIANCE

• Low-level behaviors, or competences, corresponds
  to primitive survival behaviors such as obstacle
  avoidance, while higher-level behaviors
  correspond to higher goals such as map building
  and exploring
• ALLIANCE delineates several behavior sets that
  are either active as a group or are hibernating
• Action selection is controlled through the use of
  motivational behaviors, each of which controls
  the activation of one behavior set
• Only one behavior set is active at any point, but
  other lower-level competences such as collision
  avoidance may be continually active

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Overview of ALLIANCE
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Motivational Behaviors

• Motivation provides the robots the ability to
  respond to unexpected events and robot failures
• Motivational behavior the primary mechanism for
  achieving adaptive action selection in the
  architecture
• Each motivational behavior receives input from a
  number of sources
• The input is combined to generate the output of a
  motivational behavior
• The output defines the activation level of its
  corresponding behavior set
• When the activation level exceeds a given
  threshold, the corresponding behavior set becomes
  active

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Motivational Behaviors

• Two types of internal motivations are modeled in
  ALLIANCE robot impatience and robot acquiescence
• The impatience motivation enables a robot to
  handle situations when other robots fail in
  performing a given task
• The acquiescence motivation enables a robot to
  handle situations in which it, itself, fails to
  properly perform its task
• ALLIANCE utilizes a simple form of broadcast
  communication to allow robots to inform other
  team members of their current activities
• The design of the motivational behaviors in
  ALLIANCE also allows robots to adapt to
  unexpected environmental changes which alter
  sensory feedback

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Motivational Behaviors

• The parameters controlling motivational rates of
  robots under the ALLIANCE architecture can be
  adapted over time based on learning
• L-ALLIANCE, an extension to ALLIANCE, provides
  the mechanisms for accomplishing parameter
  adaptation
• ALLIANCE architecture is developed to explicitly
  address the issue of fault tolerance amidst
  possible robot and communication failures
• While some efficiency may be lost as a
  consequence of not negotiating the task
  subdivision in advance, robustness is gained if
  robot failures or other dynamic events occur at
  any time during the mission

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Formal Model of ALLIANCE

• Problem definition
• Let the set Rr1, r2, ,rn represent the set
  of n heterogeneous robots composing the
  cooperative team, and let the set Ttask1,
  task2, , taskm represent m independent subtasks
  which compose the mission
• High-level task-achieving function corresponds
  to the functions possessed by individual robots
• In the architecture, each behavior set supplies
  its robot with a high-level task-achieving
  function
• The high-level task-achieving functions, or
  behavior sets, possessed by robot ri is referred
  to the set Aiai1, ai2,.
• The set of n functions h1(a1k), h2(a2k),
  ,hn(ank) is defined, where hi(aik) returns the
  task in T that robot ri is working on when it
  activates behavior set aik.

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Formal Model of ALLIANCE

• Threshold of Activation
• The threshold of activation of a behavior
  set is given by one parameter ?. This parameter
  determines the level of motivation beyond which a
  given behavior set will become active
• Sensory Feedback
• Provides the motivational behavior with the
  necessary information to determine whether its
  corresponding behavior set needs to be activated

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Formal Model of ALLIANCE

• Inter-Robot Communication
• ?i the rate at which robot ri broadcasts
  its current activity
• ti the period of time robot ri allows to
  pass without receiving a communication message
  from a specific teammate before deciding that
  that teammate has ceased to function
• Suppression from Active Behavior Sets
• When a motivational behavior activates its
  behavior set, it simultaneously begins inhibiting
  other motivational behaviors

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Formal Model of ALLIANCE

• Robot Impatience
• ?ij(k,t) gives the time during which robot ri is
  willing to allow robot rks communication message
  to affect the motivation of behavior set aij
• d_slowij(k,t) the rate of impatience of robot ri
  concerning behavior set aij while robot rk is
  performing the task corresponding to behavior set
  aij
• d_fastij(k,t) the rate of impatience of robot ri
  concerning behavior set aij in the absence of
  other robots performing the task hi(aij)

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Formal Model of ALLIANCE

• Robot Acquiescence
• ?ij(t) the time that robot ri wants to maintain
  behavior set aij activation before yielding to
  another robot
• ?ij(t) the time robot ri wants to maintain
  behavior set aij activation before giving up to
  possibly try another behavior set
• Motivation Calculation

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Parameter Settings

• The parameter settings in ALLIANCE strongly
  influence the global performance of the system
• The desirable characteristics of fault tolerance
  and adaptivity that are present in ALLIANCE
  should not be sacrificed while enabling increases
  in robot team efficiency
• L-ALLIANCE provides mechanisms that allow the
  robots to dynamically update their parameter
  settings based upon knowledge learned from
  previous experiences
• Assumptions
• A robots average performance in executing a
  specific task over a few recent trials is a
  reasonable indicator of that robots expected
  performance in the future
• If robot ri is monitoring environmental
  conditions C to assess the performance of another
  robot rk, and the conditions C change, then the
  changes are attributable to robot rk

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L-ALLIANCE

• Incorporates the use of performance monitors for
  each motivational behavior
• Robot ri programmed with the b behavior sets
• Aai1,ai2,,aib,
• also has b monitors
• MONimoni1,moni2,monib
• Monitor monij observes the performance of any
  robot performing task hi(aij)
• Monitor monij uses a mechanism to update the
  control parameters of behavior set aij based on
  the learned knowledge

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Action Selection Algorithm
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Parameter Settings
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Results

• The ALLIANCE architecture has been successfully
  implemented in a variety of proof-of-concept
  applications on both physical and simulated
  mobile robots
• Over 60 logged physical robot runs of the
  hazardous waste cleanup mission and over 30
  physical robot runs of the box pushing
  demonstration were completed to elucidate the
  importance issues in heterogeneous robot
  cooperation

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Hazardous waste cleanup

• The Robots
• Three R-2 robots purchased commercially from IS
  Robotics
• Mechanical drift and failure can cause them to
  have quite different actual abilities
• A radio communication system allows robot team
  members to communicate with other

• The Mission
• Two artificially hazardous waste spills in an
  enclosed room to be cleaned up
• The distinct tasks locating the two waste spills
  find-locations moving the two spills to a goal
  location move-spill (left) and move-spill
  (right) periodically reporting the team
  progress to humans monitoring the system
  report-progress

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Hazardous waste cleanup

• Behavior sets
• find-locations-methodical
• find-locations-wander
• move-spill (loc)
• report-process
• avoid-obstacles

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Experiments

• Three robots are referred as GREEN, BLUE, and
  GOLD

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Experiments no robot failure

• RP report-progress MS(L) move-spill (left)
  MS(R) move-spill (right)
• FLW find-locations-wander FLM
  find-location-methodical

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Experiments interfere with GREEN
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Experiments interfere with GREEN
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Experiments
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Experiments
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Experiments removal of BLUE
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Experiments removal of BLUE
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Discussion

• The cooperative team under ALLIANCE control is
  robust
• The cooperative team is able to respond
  autonomously to many types of unexpected events
  either in the environment or in the robot team
  without the need for external intervention
• The cooperative team needs not have a priori
  knowledge of the abilities of the other team
  members to effectively accomplish the task
• The primary weakness of ALLIANCE is its
  restriction to independent subtasks

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Conclusion

• ALLIANCE is a fully distributed, behavior based
  architecture which facilitates fault tolerant
  mobile cooperation
• ALLIANCE allows the robots to handle the
  environmental changes fluidly and flexibly
• ALLIANCE also allows robot team members to
  respond to their own failures or to failures of
  teammates, leading to adaptive action selection
  to ensure mission completion
• ALLIANCE further enhances team robustness by
  making it easy for robot team members to deal
  with the presence of overlapping capabilities on
  the team

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Final word

• Thank you
• and
• Have a great new week!