Motion Control Techniques for Collaborative Multi-Agent 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 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 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

Attractive Force

Repulsive Force

Resultant Field

Agent Path

Key Terms VLSR - Very Large Scale Robotics System Global Controller – defines the pair-wise potential laws for

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. Imposes a repulsive force on other bees Is subjected to attractive forces of the flowers

and the beehive Flowers and beehive are landmark agents.

Impose attractive and repulsive forces on the bees

Potential-Based Implementation

Agents do not make any 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 Simulation class contains main scheduler

Initialize the scenario Control the simulation rate

Map2D Simulate the environment Account for all entities Process potential fields

FlowerAgent Represent an area/object of interest Supply collectable data (nectar)

BeehiveAgent Represent a sink node Store nectar or collectable data

BeeAgent Mobile node that gather nectar Move to interest area base on potential

fields direction and magnitude DisplayFrame

Java base GUI Display movement in realtime

DataCollector 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

Configuration: Four flower with equal nectar 10 Bees total 2 Exercises, linear and square force model Performance is approximately identical

Simulation Results

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 Bid solicitation Contract award

Market-Based Collaboration

Role-Based Approach

Based on the E-CARGO model Each agent or group agents is described as a 9-

tuple <C,O,A,M,R,E,G,S0>

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

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