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Dynamic Speed and Sensor Rate Adjustment for Mobile Robotic Systems

Dynamic Speed and Sensor Rate Adjustment for Mobile Robotic Systems. Ala’ Qadi , Steve Goddard University of Nebraska-Lincoln Computer Science and Engineering Department Jiangyang Huang, Shane Farritor University of Nebraska-Lincoln Mechanical Engineering Department.

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Dynamic Speed and Sensor Rate Adjustment for Mobile Robotic Systems

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  1. Dynamic Speed and Sensor Rate Adjustment for Mobile Robotic Systems Ala’ Qadi, Steve Goddard University of Nebraska-Lincoln Computer Science and Engineering Department Jiangyang Huang, Shane Farritor University of Nebraska-Lincoln Mechanical Engineering Department

  2. Introduction: Mobile Robotic Systems • As real-time systems, computations must be completed within established response times. • As spatial systems, the computation performed and their timeliness will be dependent on: • The location of the platform in its environment. • The velocity with which the platform is moving. • The existence of objects in the environment.

  3. Challenges • Task execution requirements change as the platform moves in the environment. • Platform velocity is dependent on the rate system can collect and process data. • Dynamic changes in the environment (obstacle) might lead to overload conditions.

  4. Contributions • An abstract analysis methodology for mobile real-time systems that integrates spatio-temporal properties: • processing windows. • zone abstractions. • Dynamic adjustment algorithm: • maintains a maximum speed less than or equal to the desired speed. • maintains schedulabilty by adjusting • processing window. • platform speed.

  5. Processing windows • Processing Window: The time interval from the instant the platform starts collecting data to the moment the platform must finish processing the data. • A processing window is the deadline for execution of one or more interdependent tasks.

  6. Zones: No Motion We define a zone as the area for which the platform collects and processes sensor information, creates a map for the area and plans its path through the area. 2-Dimensional Zone Example

  7. Zones: Mobile System In Motion In motion, safety area included

  8. Zones: Definitions • Planning Point Fi =(tiF ,LiF) • Data Collection Point Bi=(tiB ,LiB) Two-Dimensional Zones • LiF =(xiF,yiF,yiF) • LiB =(xiB,yiB,yiB) • Fi =(tiF,xiF,yiF,yiF) • Bi=(tiB,xiB,yiB,yiB)

  9. Zones: Zone Processing Windows Maximal Scanning Minimal Scanning

  10. Dynamic Processing Windows • Changes in the platform environment. • Increasing the maximum possible platform speed. • Increasing performance for processing window related task.

  11. Sensor Impact on Processing Window Length • The zone processing window of the platform is dependent on sensor parameters: • number of sensor n. • set of delays between sensor readings/invocations D. • set of sensor range and sensitivity R. • set of sensor tasks execution times E. • feasibility function g is dependent on the sensors and the associated tasks and parameters. • DI, Independent delays, DR, Sensor range dependent delays

  12. Schedulabilty Impact on Processing Window Length • Any mobile real-time platform will have a set of tasks • is set of tasks associated with the zone processing window w. • is a (possibly empty) set of periodic tasks with higher priority than . • is a (possibly empty) set of periodic tasks with lower priority than .

  13. Schedulabilty Impact: Fixed Priority Scheduling

  14. Combining the sensor bound with the schedulabilty bound. • If , to find the lower bound on w, Solve • The same procedure can be extended if .

  15. Motion Impact on Processing Window Length • The maximum speed at which the platform can travel is related to the rate the environment can be scanned and signals processed. • The speed of the platform for a zone is dependent on • The radius of the zone. • The zone-processing window. • The speed of the platform in the previous zone. • The existence of obstacles in the zone.

  16. Motion Impact on Processing Window Length First Zone Z0 Beyond Z0 Motion Bound

  17. Example: 2-dimisional Constant Speed If at any plan point Fi we change the zone processing window wior change the sensor detection range ri.

  18. Motion Impact on Processing Window Length: Obstacles Exist • The distance the platform can safely move is not the zone radius. • Move safe distance between the obstacle and the platform, Xobs. • If Xobs < Di

  19. Processing Window Adjustment Algorithm

  20. Processing Window Speed/Adjustment Algorithm

  21. Companion is a robot with 24 sonar sensors, 15o apart. Case Study1: Robot Navigation Using Sonar Sensors

  22. Task Processing Graph

  23. Motion Bounds • No Obstacles • Obstacles Exist

  24. Simulation Without Processing Window/Speed Adjustment With Processing Window/Speed Adjustment

  25. Actual Test With Processing Window/Speed Adjustment Without Processing Window/Speed Adjustment

  26. Results Summary Simulation Result Summary Actual Test Summary

  27. Conclusion • We presented a method for integrating • speed requirements of a mobile robotic platform with • real-time fixed priority scheduling. • New abstractions called zones and processing windows were created. • An algorithm for the adjusting zone processing window was developed. • Improved system performance (Speed).

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