Motion Control Techniques
for Collaborative MultiAgent Activities
David Benjamin
Phuoc Nguyen
What is an Agent?
 An agent is a system situated in, and part of,
an environment, which senses that
environment, and acts on it, over time, in
pursuit of its own agenda. This agenda
evolves from programmed goals.
 The agent acts to change the environment
and influences what it senses at a later time.
Motion Control
 In the field of automation, it involves the use
of devices such as hydraulic pumps, linear
actuators, or servos to control the position
and/or velocity of an object.
 In the field of multi-agent, collaborative
systems it is the control of the position and/or
velocity of agents so that the agents can work
together to accomplish a goal.
Motion Control
Centralized Control
 A single point of control where the controller
gathers all of the information in the
environment (including the state of each
agent) and the plans the motion for the each
agent
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Central controller has high level complexity
Requires a high bandwidth communication link
May be impractical for battery powered agents
Distributed Control
 Each agent determines its motion by sensing
the environment and then reacting according
to a set of rules
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Agents are unaware of the agendas of other
agents.
Does not require communication with a central
controller.
Simpler implementation.
Flexible.
Social Potential Forces
 Initially used for obstacle avoidance.
 Obstacles and agents are assigned negative charges
 Goal destinations are assigned positive charges
 A maximum electric field is formed when the agent
and the obstacles are within close proximity (repelling
forces).
 A minimum electric field is formed when the agent
and the obstacles are within close proximity
(attractive forces).
 The agent will naturally avoid obstacles while it
moves toward its goal destination.
Social Potential Forces
Attractiv
e Force
Repulsiv
e Force
Resultan
t Field
Agent
Path
Key Terms
 VLSR - Very Large Scale Robotics System
 Global Controller – defines the pair-wise potential laws for
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ordered pairs of components
Global Control Force – resultant force calculated by each robot.
Global in the sense that it coordinates the agents and
determines the distribution of the agents throughout the system.
Local Control Force – The individual attractive and repulsive
forces sensed by an agent.
Leading Agents – Mobile agent with a preprogrammed path.
Landmark Agents – Have a fixed position. Are immune to social
potential forces, but imposes social potential forces on ordinary
agents.
Ordinary Agents – Mobile agent that is subjected to social
potential forces and also imposes social potential forces on
other agents.
Beehive Simulation
Each bee is an ordinary agent.
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Imposes a repulsive force on other bees
Is subjected to attractive forces of the flowers
and the beehive
 Flowers and beehive are landmark agents.
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Impose attractive and repulsive forces on the
bees
Potential-Based Implementation
 Agents do not make any
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decisions
All movements are
triggered by active
forces
All agents implement
their own force model
Flowers and beehive
have attractive forces to
each bee
Bees have repel force
Design
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Simulation class contains main scheduler
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Map2D Simulate the environment
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Mobile node that gather nectar
Move to interest area base on potential
fields direction and magnitude
DisplayFrame
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Represent a sink node
Store nectar or collectable data
BeeAgent
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Represent an area/object of interest
Supply collectable data (nectar)
BeehiveAgent
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Account for all entities
Process potential fields
FlowerAgent
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Initialize the scenario
Control the simulation rate
Java base GUI
Display movement in realtime
DataCollector
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Record simulation data
Export data to excel spreadsheet
Load Balancing
 Mechanism to prevent swarming affect
 Each flower have a queuing service. If queue is full,
attractive force is greatly reduce
 Attractive force has an inverse distance square
relationship
 Bees have a repel force on each other
 Bees have a maximum load capacity it can carry
 Force threshold
 As the bee capacity increase, its attraction to the hive
also increase. And the attraction to flowers will
decrease. Once hive attraction overtake the flower by
a certain threshold, the bee will change direction and
head back to the hive.
Movement Model
FL
Beehive
FL
FL
Simulation Results
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Configuration: Four flower with equal nectar
10 Bees total
2 Exercises, linear and square force model
Performance is approximately identical
Simulation Results
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Configuration: Four flower with variable nectar
10 Bees total
2 Exercises, linear and square force model
Performance is approximately identical
Market-Based Collaboration
 Collaborative mechanism employed by the
Autonomous Collaborative Mission Systems
(ACMS).
 Aimed at controlling groups of heterogeneous
agents.
 Two stage process
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Bid solicitation
Contract award
Market-Based Collaboration
Role-Based Approach
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Based on the E-CARGO model
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Each agent or group agents is described as a 9tuple
<C,O,A,M,R,E,G,S0>
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C is a set of classes
O is a set of objects
A is a set of agents
M is a set of messages
R is a set of roles
E is a set of environments
G is a set of groups
S0 is the initial state of the system
Role-Based Approach
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Roles specify how an agent behaves at a
specific context within a limited period
Each agent will only respond to a subset of
messages that are defined by its role.
Each agent will respond differently to the
same message based on its role.
Each agent can be programmed to play many
different roles based on the state of the
environment and/or the messages it receives.
Demo
Questions?
Citations