1. 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. 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. 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. 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. 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. 6 Zones: No Motion 2-Dimensional Zone Example 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.

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

8. 8 Zones: Definitions  Planning Point F i =(t i F,L i F )  Data Collection Point B i =(t i B,L i B ) Two-Dimensional Zones  L i F =(x i F,y i F, i F )  L i B =(x i B,y i B, i B )  F i =(t i F,x i F,y i F, i F )  B i =(t i B,x i B,y i B, i B )

9. 9 Zones: Zone Processing Windows Maximal ScanningMinimal Scanning

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

11. 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 . 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. Independent delays, R, Sensor range dependent delays

12. 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. 13 Schedulabilty Impact: Fixed Priority Scheduling

14. 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. 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. 16 Motion Impact on Processing Window Length First Zone Z 0 Beyond Z 0 Motion Bound

17. 17 Example: 2-dimisional Constant Speed If at any plan point F i we change the zone processing window w i or change the sensor detection range r i.

18. 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, X obs.  If X obs < D i

19. 19 Processing Window Adjustment Algorithm

20. 20 Processing Window Speed/Adjustment Algorithm

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

22. 22 Task Processing Graph

23. 23 Motion Bounds  No Obstacles  Obstacles Exist

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

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

26. 26 Results Summary without Algorithmwith Algorithm t total (s) 96.4873.17 38.2048.02 76.40%96.04% without Algorithmwith Algorithm t total (s) 85.2065.53 29.7436.09 29.9736.85 59.48%72.18% 59.97%73.7% Simulation Result Summary Actual Test Summary

27. 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).

28. 28 Processing Window Adjustment Algorithm

29. 29 Motivation  Unlike traditional real-time applications, the platform requires support for: interdependent tasks with inter-delays. relating the deadline of a task to a spatial concept. dynamically changing execution requirements due to the environment.