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OBSTACLE AVOIDANCE. APPLIED BEHAVIOR-BASED CONTROL IN AUVs. NAME DATE TITLE. Anton Gravestam 12 15 2008 OBSTACLE AVOIDANCE. Overall Control Strategy Mission – Planning and Execution Behavior-Based Control Obstacle Avoidance Priority Tuning Present and Future References. OUTLINE.

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obstacle avoidance

OBSTACLE AVOIDANCE

APPLIED BEHAVIOR-BASED CONTROL IN AUVs

NAME

DATE

TITLE

Anton Gravestam

12 15 2008

OBSTACLE AVOIDANCE

outline
Overall Control Strategy

Mission – Planning and Execution

Behavior-Based Control

Obstacle Avoidance

Priority Tuning

Present and Future

References

OUTLINE
overall control strategy
Control decomposition into functional layers with different temporal requirements and levels of abstraction.

Degree of autonomy determined by number of layers under vehicle control.

Decoupling of high level control functionality from specific vehicle platform

Behavior based control at the core of extendable reactive autonomy.

Planning

Deliberative layer

Mission plan

Behavior-basedcontrol

Reactive layer

Low-levelcontrol layer

PID-control

Control signals

Actuators

OVERALL CONTROL STRATEGY

Reference values(heading, speed etc.)

mission planning and execution
MISSION - PLANNING AND EXECUTION
  • Missions consist of Actions:
    • Sequential discrete events
    • Well-known transition models
    • For example: Transport, MineSearch, Docking
  • Actions consist of Behaviors:
    • Parallel continuous control functions
    • Activated during runtime
    • Example: AvoidObstacle, GotoWaypoint(W), GetGPS-position

Transport

MineSearch

Transport

Docking

1

2

3

4

Docking

AvoidObstacle

AvoidObstacle

AvoidObstacle

FollowSeaBed

GetGPS-position

GetGPS-position

GotoWaypoint(W1)

GotoWaypoint(W2)

FollowSearchPattern

behavior based control

Sensordata

Behavior

Priority

p1

Arbitration

Sensordata

Behavior

p2

p3

Sensordata

Behavior

BEHAVIOR-BASED CONTROL
  • Each behavior can voice its opinion on best course of action
  • Behavior responses as utility functions
  • An arbitration mechanism coordinates behaviors to maximize utility
  • Dynamic activation level and static priority determines behavior influence.
  • Reference values passed to low-level control system:roll, pitch, heading and speed in x, y, z.

Referencevalue

why use behavior based control
WHY USE BEHAVIOR-BASED CONTROL?
  • Good approach for extendable autonomy functions.Capabilities can be added or improved by introducing new behaviors or by recombining existing.
  • Enable a vehicle to fulfill multiple requirements in parallel E.g. Avoiding obstacles while following waypoints.
  • Can accommodate for dynamic environments and unforeseen events unknown when planning
  • Widely used approach for autonomous mobile robotic control.
  • Overall vehicle behavior can be altered for different mission objectives by tuning behavior priorities.
  • Multiple behaviors concerned with the same thing can be present at the same time. Use high performance behaviors when safe, fall back to security behaviors when hazard risk is high.
behavior based control example
BEHAVIOR-BASED CONTROL: EXAMPLE
  • 1: Track following:Follow track closely for best sonar coverage and platform stability.
  • 2: Waypoint navigation:Ensure that the overall goal of reaching the next waypoint is met
  • 3: Obstacle avoidance:Steer the vehicle clear of obstacles. Activation rises with hazard proximity.
  • 4: Avoid past:Influences the vehicle to favor a new path to avoid getting stuck in circular behaviors
  • 5: Emergency stop:Influences the vehicle cruising speed to decrease with obstacle proximity. Ultimately forces the vehicle to a full stop if to close.

Activation level

Hazard Risk

Distance to Obstacle

t

obstacle avoidance8

Occupancy grid

Localization

FLS

IMU

GPS

DVL

Sensor Stimuli

Utility Function Response

Obstacle Avoidance

OBSTACLE AVOIDANCE
  • Behavior reacts to obstacle map (Occupancy grid) and vehicle position/orientation (Localization)
  • Response is formed as a utility function with minimums at directions of obstacles
obstacle avoidance9
Sensor Abstraction:

Decouple high-level software from specific sensor hardware

High level control functions (e.g. Obstacle avoidance) use high level sensor abstractions.

Any number of different sensors can be used to enhance sensor data

Occupancy grid

Localization

FLS

IMU

GPS

DVL

OBSTACLE AVOIDANCE
obstacle avoidance10
Occupancy Grid

Short term 3D mapping of the surrounding environment used in real-time for obstacle avoidance.

Memory enables reaction to obstacles no longer in field of view.

Obstacle memory fades over time to accommodate for dynamic environments and drift in position estimate.

Can be created from any type of proximity sensor.

Use single sonar or multiples ones.

OBSTACLE AVOIDANCE
obstacle avoidance11
OBSTACLE AVOIDANCE
  • One utility function for each degree of freedom represents a behaviors voice

Behavior response from Track Follow behavior

obstacle avoidance12
OBSTACLE AVOIDANCE
  • Responses are weighted together and the maximum is chosen as the response to send to control system

Behavior response from Track Follow behavior and Obstacle Avoidance weighted together

obstacle avoidance13
OBSTACLE AVOIDANCE
  • Increased activation of Obstacle avoidance as Obstacle approaches

Behavior response from Track Follow behavior and Obstacle Avoidance weighted together

priority tuning
PRIORITY TUNING
  • Tuning individual behaviors priority gives vehicle different ”personalities”

Bold Behavior

Cautious Behavior

Samples of path chosen with to sets of behavior priorities. To the left: High priority on obstacle avoidance.

To the right: Higher priority on track following.

putting it all together using pub sub middleware technology

Middleware

Node 1

subscribe

Node 3

Topic 1

publish

subscribe

publish

publish

Topic 2

subscribe

Node 2

PUTTING IT ALL TOGETHERUSING PUB/SUB MIDDLEWARE TECHNOLOGY
  • Open distributed architecture - flexible, transparent and scalable
  • Decentralized architecture, no central services/single point of failure
  • Asynchronous messaging with Quality of Services
  • Maps well to Behavior-Based Control
  • Enables isolation of time and safety critical functions
present and future

Operator controlled

Autonomous

Autonomous

PRESENT AND FUTURE
  • Present: Autonomous control up to reactive layer
  • Future development should focus around intelligent autonomous replanning of missions to solve complicated hazard situations such as deep caves or under ice navigation

Present

Future