Real time support for mobile robotics
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Real-Time Support for Mobile Robotics. K. Ramamritham (+ Li Huan, Prashant Shenoy, Rod Grupen). Background. A team of mobile robots Collaborate with each other to achieve a common goal Search for trapped people in a burning building. Sensors. Processor. Wireless link. Motivation.

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Real-Time Support for Mobile Robotics

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Real time support for mobile robotics

Real-Time Support for Mobile Robotics

K. Ramamritham

(+ Li Huan, Prashant Shenoy, Rod Grupen)


Background

Background

  • A team of mobile robots

  • Collaborate with each other to achieve a common goal

    • Search for trapped people in a burning building

Sensors

Processor

Wireless link


Motivation

Motivation

  • To accomplish a search:

    • Sensor tasks : acquire sensor data

    • Processing tasks : process sensor data

    • Motor tasks : drive the movement of robots


Problem

Problem

  • Dynamic environment

    • Robots move as a team

    • Team may change in the size

  • Hardware constraints for some tasks

    • Sensor tasks are pre-allocated to robots

  • Which robots run which tasks and when?

    • Where to allocate processing tasks?

    • When to run tasks?


Outline

Outline

  • Motivation

  • System model

  • Allocation and scheduling algorithms

  • Experimental results

  • Related work and conclusions


Problem model

Leader

Follower

Problem Model

  • Pair-wise relationship to control the movement

  • Leader Follower

  • Two control strategies

    • Push:

    • follower specifies the search area of the leader

    • Pull:

    • leader searches the area, pulls the follower behind him


Task graph push

Motor

Leader

P3

P1

P2

Motor

Follower

Task Graph (Push)

Construct the map of walls

IR

Compute the next location

of the leader

Choose search area

Push

POS


Task graph pull

Task Graph (Pull)

Leader

IR

Motor

Construct the map of walls

Choose search area

P3

P1

Pull

P2

Compute the next location

of the follower

Motor

POS

Follower


Task model and goal

Task Model and Goal

  • Tasks are abstracted using task graphs

  • Periodic tasks with constraints :

    • Deadline : each instance has a relative deadline

    • Location : sensor tasks are pre-assigned to robots

    • Precedence :

      sensor tasks  processing tasks  motor tasks

  • Goal:

    • Allocate and schedule tasks on robots

      • All constraints are satisfied


Why is this a hard problem

Leader

Push

Pull

Follower

Push

Leader

Pull

Follower

Why is this a Hard Problem?

  • Possible strategies dynamically change as the size changes

    • Increase exponentially as the team size scales

  • Need to efficiently find a feasible strategy online

  • {Push, Push}, {Push, Pull}, {Pull, Push}, {Pull, Pull}


Overall approach

Overall Approach

  • Allocate tasks to appropriate robots

    • Minimize communication

    • Balance processor workload

  • Find a feasible schedule

    • Deadlines are met

    • Precedence constraints are satisfied

Can smart allocation improve schedulability?


Task allocation

Task Allocation

Step 1 : Choose an unallocated task Tj

Step 2 :Choose an appropriate processor

Communication Cost Ratio (CCR):

comm_cost(Ti Tj )

CCRi, j =

Ei: Execution time of Ti

Ei + Ej


Step 1 how to choose a task

Step 1: How to Choose a Task

  • Consider tasks such that all their preceding taskshave been allocated

  • Try to minimize communication cost

  • Two techniques to choose Tj :

    • Greedy: consider individual cost

    • Aggressive: consider total cost from the same processor


Step2 how to allocate a task

Step2: How to Allocate a Task

If Tj is chosen, and TiTj

  • Try to balance and minimize workload

  • Assign Tj to the same processor as Ti

    • So long as the processor does not become the most heavily loaded processor

    • Network communication between Ti and Tj is eliminated

  • Otherwise, put Tj to the processor with the least utilization


Allocation example

Choose T4

ChooseT5

Step 2: find the robot

Currently : U1 = U2 = 1/3

Greedy:Assign T4 to Robot 1

Aggressive:Assign T5 to Robot 2

Allocation Example

Step 1: Consider T4,T5

Robot 2

Robot 1

Greedy :

Aggressive:

T1

T2

T3

CCR1,4 = 1/4

CCR2,5+CCR3,5=1/3

1/6

1/5

1/6

1/4

1/5

T4

T5

1/3


Making scheduling decisions

Making Scheduling Decisions

  • Have allocated tasks to processors, need to find a feasible schedule

  • Possible heuristic functions

    • EDF (Min_D)

    • Minimum laxity first (Min_L)

    • Earliest-start-time first (Min_S)

    • Weighted combinations of

      {deadline, earliest-start-time, laxity}


Outline1

Outline

  • Motivation

  • Problem setting

  • Allocation and scheduling algorithms

  • Experimental results

    • Simulation results

    • Application analysis

  • Related work and conclusions


Simulation settings

Simulation Settings

  • Homogeneous system

  • Number of processors and tasks are varying

  • Task sets are randomly generated, each

  • Metric: SuccessRatio (SR):

    N succ: number of successfully scheduled task sets

    N: total number of tested task sets


Scheduling heuristic

Scheduling Heuristic

  • Min_S is the best single heuristic

    • Encode precedence constraints

  • Min_D + W×Min_S is the best overall

    • Both deadline and precedence are taken into account


Performance of allocation algorithms

Performance of Allocation Algorithms

Aggressive outperforms the other methods.

  • The improvement is larger when the resources are tight.


Analysis with mobile robots

Analysis With Mobile Robots

  • Three Robots, period = 220 ms

  • {Push,Pull} is not feasible

  • Metrics to choose the optimal one

    • Min max laxity : {Pull, Push}

  • Prune the infeasible strategies as the team size scales

Completion time for tasks on each robot


Related work

Related Work

  • Task allocation and scheduling in distributed environment.

    • Branch-and-bound search [Peng 97]

    • Period-based method of load partitioning and assignment [Abdelzaher 00]

    • Static allocation for tasks with duplication and precedence constraints [Ramamritham 95]

  • Utilization bound for schedulability analysis

    • Uniprocessor, independent tasks [Liu&Layland 73]

    • Multiprocessor , P-fairness scheduling [Baruah 96]

    • EDF, RMA [Andersson 01], [Baruah 01], [Funk 01], [Goossens 02], [Srinivasan 02]


Conclusions and future work

Conclusions and Future Work

  • A team of mobile robots to achieve a goal

    • Allocate and schedule real-time tasks with constraints for dynamic robotic teams

  • Smart allocation of tasks can improve the schedulability of the whole system

  • Future work :Heterogeneous systems

    http://lass.cs.umass.edu/


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