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The MPAS Project Multi-agent Pathfinding Algorithms Simulator

Technical Advisor : Mr. Roni Stern Academic Advisor : Dr. Meir Kalech Team members : Amit Ofer Liron Katav Project Homepage : http://mpasproject.weebly.com. The MPAS Project Multi-agent Pathfinding Algorithms Simulator. Introduction.

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The MPAS Project Multi-agent Pathfinding Algorithms Simulator

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  1. Technical Advisor : Mr. Roni Stern Academic Advisor : Dr. Meir Kalech Team members : AmitOfer LironKatav Project Homepage : http://mpasproject.weebly.com The MPAS Project Multi-agent Pathfinding Algorithms Simulator

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. System Architecture - High level MPAS External GIS Geographical map environment Grid map environment

  9. Systemarchitecture User input State Change ControllerLayer View Changes Input • Presentationlayer • Algorithm Layer -Method Invocation -Events

  10. Main Functional Requirements • Choose the number of agents • In the grid map environment: • Choose the size of the grid map • In the geographical map environment: • Choose the map • Load grid maps • Save grid maps • Clear map • Sets the starting and finishing cells for each agent • Set blocking cells

  11. Main Functional Requirements • Choose the algorithm to be tested • Choose the heuristic to be used • Start the simulation • Stop the simulation • Running the simulation Step by step • Generate random scenario • Restart simulation

  12. 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).

  13. 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.  

  14. 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.

  15. Usage Scenarios

  16. Usage Scenarios

  17. Project’s status • ARD • Prototype v1.0 includes : • A-star implementation with 2 agents on a grid environment

  18. References • A- Star alorithmhttp://en.wikipedia.org/wiki/A_star • D. Silver, 2005. Cooperative Pathfinding. • T. Standley, 2010. Finding Optimal Solutions toCooperative Pathfinding Problems .

  19. Thank you for listening The End

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