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Using Real-time Awareness to Manage Performance of Java Clients on Mobile Robots Andrew McKenzie, Shameka Dawson, Quinton Alexander, and Dr. Monica Anderson.

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Presentation on theme: "Using Real-time Awareness to Manage Performance of Java Clients on Mobile Robots Andrew McKenzie, Shameka Dawson, Quinton Alexander, and Dr. Monica Anderson."— Presentation transcript:

1 Using Real-time Awareness to Manage Performance of Java Clients on Mobile Robots Andrew McKenzie, Shameka Dawson, Quinton Alexander, and Dr. Monica Anderson Department of Computer Science University of Alabama Tuscaloosa, AL USA This work was supported in part by the following NSF grants: IIS and CCF

2 Outline Motivation Related Works Extension Features Implementation Experimental Setup Results Conclusion/Future Work

3 Motivation Timing (as in frequencies for tasks) requirements are not available in free open- source Mobile Robot Architectures [Kramer et al 2007] – Timing requirements are required in control systems for robotic arms, aircraft flight controllers, and automobile controllers

4 Related Work Player [Gerkey et al 2001] Subsumption Architecture [Brooks 1991] ARIA [Konolige 2002] OROCOS [Bruyninckx 2001] Chimera II [Stewart et al 1992]

5 Extension Features Specify and execute each behavior periodically Manage data production and consumption within the framework Initial Goals – resource utilization – memory management – reporting

6 Implementation Modified Player to include the proposed extensions The extensions are beneficial to any language Written in Java

7 Why use Player? language bindings for C, C++, Java, Python, Ada, Lisp, Ruby … Open source Platform independent Does not provide a framework for behaviors Easily modified to include our extensions

8 Why Java? Suggested that programs written in Java take less time to code and/or contain fewer errors [Phipps 1999] Garbage collection (good and bad) – Memory management – Can cause indeterminism

9 Player Server Robot Sensors PlayerDataTask TCP/IP Monitor Tasks schedules 1 1..n 1 1 starts SensorData Data update Multiple outputs Associations Communication Real-time aware Framework

10 Experimental Setup K-team Koala Robot Acces I/O ETX-Nano computer – Intel Core Duo 1.66GHz – 2GB of RAM – 8GB compact flash – Runs Fedora 10 Hokuyo URG laser range finder

11 Experiment room (9.6m x 6.2m) with four waypoints

12 Experiment tasks WayPointPlanner (Waypoint) – moves the robot through a series of waypoints LaserObstacleAvoidance (Laser) – avoids obstacles sensed via the laser OccupancyGridMapping (Occupancy) [Elfes 1989] – uses position and laser data to build and maintain an occupancy grid map DijkstraPathPlanner (Planner) [Dijkstra 1959] – plans a path from the current position to a goal (waypoint) using the occupancy grid map

13 Real-time aware Framework used for Experiments

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18 Sets were not statistically significant in terms of distance from finish Sets 1 and 4, and Sets 1 and 5 were statistically significant in terms of total run time Sets 2 and 4, Sets 3 and 4, and Sets 4 and 5 had confidence intervals of 82%, 85% and 79% in terms of total run time

19 Future Work Compare performance on a RTOS using Sun’s Java RTS Compare performance between C++ and Java

20 Conclusion Presented a means of extending a behavior based paradigm to incorporate temporal awareness – Written in Java and added to the Player interface T-tests on the Experimental data showed that you can effect the overall performance by adjusting the timing of the underlying behaviors

21 Questions

22 Framework using Java RTS on Solaris


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