Consumer robotics: Fiction or reality? Paolo Pirjanian, Chief ScientistEvolution Robotics, Inc.November 18, 2003

OverviewThe challenges of consumer robotics

Key technology needs

Solutions provided by Evolution Robotics

Building a viable B2B robot company

Challenges of consumer roboticsChallenge 1: Cost efficiencyChallenge 2: Reliability (Real real-world robotics)Challenge 3: Test and validationChallenge 4: Power efficiencyChallenge 5: Miniaturization Ultimate: Meeting customer expectations.

Key technology needsNavigation- Low-cost localization (and mapping)- Reliable obstacle detection sensors/technologies- Complete coverage- Self-dockingHuman-Robot Interaction - Voice recognition from a distance in noisy environments- Human identification- Gesture recognition- Approach and follow (come here, follow me)- Affective computing

Phase 1: Now we can do interesting stuff (specialized hardware platform): E.g., clean, patrol, deliver, gather information, entertain,

Key technology needsFlexible/generic hardware platform- Flexible manipulation- Pick and place- Reach around and grab- Stair climbing- Variable terrain, Indoor/outdoor mobility

Phase 2: Now we can start doing some real interesting stuff (flexible hardware platform): E.g., Fetch, deliver, fix, assemble, cut, wash, 4.Robot-Robot Interaction- Communication- Task allocation- Coordination and Collaboration

5.Learning

Phase 3:Sky is the limit.

Evolution RoboticsTechnology Solutions and Services

Technology solutions providedby Evolution Robotics Object recognition: Reliable object recognition for navigation, HRI, servoing, manipulation

vSLAM: Low-cost, vision-based localization and mapping

Architecture: Cross platform middleware for system integration

Object Recognition

Approach:Extracts 1000 SIFT features of each object. A very small subset of those features with the right configuration is required for identification of the object. Estimation: Identification can provide the name of the object and the full pose of the camera with respect to the object.

Example applicationsVisual servoing, navigation, dockingEdutainment: Reading book, visual programmingManipulationSLAM

Sony Aibo uses ER Vision

Visual Simultaneous Localization & MappingFuses data from single USB camera and odom

Use sparse range of unique features. Dense range is not necessary.

Takes pictures of unique locations to build a map and uses those to estimate robots position

Accuracy of about +/- 25cm in x,y, and about 5 degrees in heading

vSLAMOdometrySLAMActual Path

Representative Images

ER Software Platform (Integration middleware)Architecture that runs on many platformsHas been embeddedCross OS (3 OSs)Highly independent of robot

(Near) Future workDevelop < $100 navigation systemSLAMPath planning/executionObstacle detection/avoidanceHazard detection/avoidanceSelf-docking and charging

Contact informationwww.evolution.comWhitepapers, videos, press releasesJob descriptionsSales

paolo@evolution.com

Frame rate proportional to CPU power (5 fps on a Pentium III)Low-cost hardware requirementsUSB cameras (as opposed to laser range finder)Reliable to changes in environment (moved furniture, moving people, etc.)