The MPAS Project Multi-agent Pathfinding Algorithms Simulator

Technical Advisor : Mr. Roni Stern Academic Advisor : Dr. Meir KalechTeam members :

Amit Ofer Liron Katav

Project Homepage : http://mpasproject.weebly.com

Introduction Path finding refers to the problem of searching

the shortest route between two points.

Multi-agent path finding problem involves navigating units from their starting position to their respective goals, whilst going around any static obstacles and other moving units along the way.

Introduction

The problem is becoming increasingly

important in many real-life applications, including motion planning in robotics, air traffic control, vehicle routing, military operation planning and computers games.

Problem domain

The standard algorithm for this problem is the A-star (A*) algorithm.

A star is an extension of Dijkstra’s algorithm, A* achieves better performance (with respect to time) by using heuristics.

Problem domain

The problem with the A-star algorithm is that its complexity grows exponentially with the number of mobile units on the map, making it not practical for real time applications.

For this reason the modern research focuses on finding a more efficient algorithms that solve the multi-agent pathfinding problem.

Vision

Our goal is to develop a simulator that will help to observe the different behaviors and compare the performance of various multi-agent pathfinding algorithms.

The algorithms that will be tested are: A-star (A*), 1968. Hierarchical Cooperative A* (HCA*) ,2005. Operator Decomposition + Independence

Detection, 2010.

Vision

The algorithms will be tested on two environments:

Grid map – a tiled based map where each unit can move to one of the 8 adjacent tiles.Geographical map – a real world map where the mobile units are limited to moving on the roads.

System Architecture - High level

Geographical map

environmentGrid map

environmentExternal

GIS

MPAS

System architecture

Algorithm Layer

Presentation layer

Controller Layer

-Method Invocation-Events

User input

ViewChanges

Input

State Change

Main Functional RequirementsChoose the number of agentsIn the grid map environment:

Choose the size of the grid mapIn the geographical map environment:

Choose the mapLoad grid mapsSave grid mapsClear mapSets the starting and finishing cells for each agentSet blocking cells

Main Functional RequirementsChoose the algorithm to be testedChoose the heuristic to be usedStart the simulationStop the simulationRunning the simulation Step by stepGenerate random scenarioRestart simulation

Main Non Functional Requirements

Speed The system should launch in less than 1 minute. It gives an output in no more than 15 minutes (for an average

problem’s size). Capacity

Up to 1 Million vertices (1000 *1000 on grid or 1 Million on geographical-map)

Up to 100 agents that will run simultaneously. Portability

The system should operate on Linux and Windows (XP/Vista/7). The system should be able to run on a standard pc computer (though

calculation times may vary according to system specs).

Main Non Functional Requirements Usability

The system GUI should be user-friendly and easy to use. The system should be simple to manage for the common

user. The learning pace of the system should be quick. 

Availability The system should be able to operate at any time of day

and no matter the amount of applications running at the background of the Operating System.  

Main Non Functional Requirements Extensibility

All algorithms will implement a predefined interface. Thus the simulator will be easy to extend by adding more algorithms that will implement this interface.

Platform Constraints The application will be developed in Java. The computer that will run the system should not be older than

3 years and include JRE and java version 1.6 or higher.

Usage Scenarios

Usage Scenarios

Project’s status

ARD Prototype v1.0 includes :

• A-star implementation with 2 agents on a grid environment

References

A- Star alorithm http://en.wikipedia.org/wiki/A_star

D. Silver, 2005. Cooperative Pathfinding. T. Standley, 2010. Finding Optimal

Solutions toCooperative Pathfinding Problems .

The End

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