The Robotics Institute

Cognitive Colonization

The Robotics InstituteCarnegie Mellon University

Bernardine Dias, Bruce Digney, Martial Hebert, Bart Nabbe, Tony Stentz, Scott Thayer

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Presentation Outline

Requirements Software Architecture Perception and Mapping Communal Learning Robot Test Bed Status and Future Work

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Requirements

is robust to individual robot failure; does not depend on reliable

communications; can perform global tasks given the limited

sensing and computational capabilities of individual robots;

learn to perform better through experience.

Distributed robotics for small-scale mobile robotscalls for a software system that:

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Cognitive Colonization Paradigm

dynamically assigning robots to tasks and checkpointing data;

treating communication as an opportunistic resource;

aggregating resources by distributing the computational and perceptual load across the group of robots;

sharing learned behaviors (both individual and group) between all robots.

The proposed software system addresses these requirements by:

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Software Architecture Models

Centralized Distributed

• optimal• intractable• brittle• sluggish• communication heavy

• suboptimal• tractable• robust• nimble• communication light

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Free Market Architecture

Robots in a team are organized as an economy

Team mission is best achieved when the economy maximizes production and minimizes costs

Robots interact with each other to exchange money for tasks to maximize profit

Robots are both self-interested and benevolent, since it is in their self interest to do global good

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Simple Reasoning

Robot 1

Robot 2

Task A = 120 Task B = 180

50

75

110

100

Robot 1 profit = 20Robot 2 profit = 30

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More Complex Reasoning

Robot 1

Robot 2

Task A = 120 Task B = 180

50

75

110

100

60

Subcontract: (150 + 110) / 2 = 130Robot 1 profit: 40 (20)Robot 2 profit: 50 (30)

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Distributed Mapping Example

Operator Exec

<-- Revenue paid

Tasks performed -->

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Distributed Mapping Roles

Unattached Robot

SingleRobot

Command Unit

Mapping Squad

Mapping Squad

Communications Squad

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Architectural Framework

Roles

Resources

Negotiations

Locomotor Sensors CPU

Mapper Comm Leader

Exec

Radio

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Simple Mapping Example

Initial Final

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Complex Mapping Example

Initial Final

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Simulated Mapping

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Simulator Movie

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Simulated Mapping

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Architecture Features

Revenue, cost and profit Negotiation and price Competition vs. cooperation Role determined via comparative

advantage Self organization Learning and adaptation

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Map Reconstruction Objectives

Reconstruction of 3-D map from multiple robots Unknown or imprecise relative position Recovery of positions and structure Map reconstruction for operators

Robot A

Robot B

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Typical Environment

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Map Reconstruction: Approach

Approach: Feature extraction Initial feature matches Recovery of epipolar geometry Filtering of matches by re-projection Recovery of motion and 3D structure

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Feature Map

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Feature Matching and Depth Recovery

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Feature Map

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Communal Learning

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Robot Death and Sacrifice

Quickly learning causes of robot death required for colony survival

‘Buddy System’ used to preserve fatal situations and actions

When robot sacrifice is required maximal cause of death information will be extracted

Causes of death are high value commodities and quickly disseminated through the colony

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Robots in Action

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Current Status

Five working robot test beds with navigation, obstacle avoidance, point-to-point communication, and image streaming

First version of software architecture working for distributed coverage tasks

First version of cooperative stereo implemented with automatic feature selection

Prototype colony interface designed

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Next Steps

Port the architecture to the real robots Extend the architecture to support all

robot roles needed for distributed mapping

Add learning for behavior parameters and transaction confidences

Perform map integration from multiple sensing points