Motion Control Techniques for Collaborative Multi-Agent Activities

1. Motion Control Techniques for Collaborative Multi-Agent Activities David Benjamin Phuoc Nguyen

2. 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.

3. 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.

4. Motion Control

5. 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

6. 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.

7. 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.

8. Social Potential Forces Attractive Force Repulsive Force Resultant Field Agent Path

9. 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.

10. 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

11. 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

12. 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

13. 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.

14. Movement Model FL Beehive FL FL

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

16. Simulation Results • Configuration: Four flower with variable nectar • 10 Bees total • 2 Exercises, linear and square force model • Performance is approximately identical

17. 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

18. Market-Based Collaboration

19. 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

20. 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.

21. Demo

22. Questions?

23. Citations