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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. - PowerPoint PPT Presentation
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1
Dynamic Speed and Sensor Rate Adjustment for Mobile Robotic Systems
Ala’ Qadi, Steve GoddardUniversity of Nebraska-Lincoln
Computer Science and Engineering Department
Jiangyang Huang, Shane FarritorUniversity 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
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.
B
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F
ii
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ttwrD
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7
Zones: Mobile System
In MotionIn motion, safety area included
MiSABrD −−=RadiusZone
8
Zones: Definitions
Planning Point Fi =(tiF ,Li
F)
Data Collection Point Bi=(tiB ,Li
B)
Two-Dimensional Zones
LiF =(xi
F,yiF,i
F)
LiB =(xi
B,yiB,i
B)
Fi =(tiF,xi
F,yiF,i
F)
Bi=(tiB,xi
B,yiB,i
B)
9
Zones: Zone Processing Windows
Maximal Scanning Minimal Scanning
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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 . 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
),,( ≥ Engw
),,,( RIEngw ≥
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
.
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13
Schedulabilty Impact: Fixed Priority Scheduling
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21
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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 .
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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
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17
Example: 2-dimisional Constant Speed
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If at any plan point Fi we change the zone processing window wi or change the sensor detection range ri.
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max
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
01
=−
),,,(max obsiii
Xwvvf
19
Processing Window Adjustment Algorithm
Dynamic ProcessingWindow Adjustment
AlgorithmEnvironmentRequirementParameters
Sensor RequirementParameters
SchedulabilityRequirements
Minimum Processing Window Length
Maximum PerformanceParameters
Scheduler
20
Processing Window Speed/Adjustment Algorithm
At the end of w(At the planning point)
Obstacle Exist
No Obstacle
No
Yes
No Yes
No Obstacle
No Yes
Yes
No
AdjustSpeed
YesNo
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21
Case Study1: Robot Navigation Using Sonar Sensors
Companion is a robot with 24 sonar sensors, 15o apart.
22
Task Processing Graph
n
τtMapTask
PlanTask
So nar
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Receive
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reenRIEng ++⋅+++⋅= )(),,,(
3402τ
DeadReckoning
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wT
hpT
23
Motion Bounds
No Obstacles
Obstacles Exist1
1
+
−−⋅−=
i
Miii
i wSwvrv
max
obs
ii
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obsiiiX
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Xwvvf −−⎟⎠⎞
⎜⎝⎛ ++−= −−
− 22
2
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1 max
max
max),,,(
24
Obstacle
Robot Body
Sonar SensorRing
Robot Path
Zone Boundary
Sonar Range Boundary
Simulation
Obstacle
Robot Body
Sonar SensorRing
Robot Path
Zone Boundary
Sonar Range Boundary
Without Processing Window/Speed
Adjustment With Processing Window/Speed
Adjustment
25
Actual Test
Without Processing Window/Speed
Adjustment With Processing Window/Speed
Adjustment
26
Results Summary
without Algorithm with Algorithm
ttotal (s) 96.48 73.17
38.20 48.02
76.40% 96.04%
without Algorithm with Algorithm
ttotal (s) 85.20 65.53
29.74 36.09
29.97 36.85
59.48% 72.18%
59.97% 73.7%
(cm/s)v
(%)/desired
vv
(cm/s)v
(%)/desired
vv
(cm/s)actual
v
(cm/s)/desiredactual
vv
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).