Autonomous Robots Key questions in mobile robotics What is around me? Where am I ? Where am I going ? How do I get there ? Alternatively, these questions correspond to Sensor Interpretation: what objects are there in the vicinity? Localization: find your own position in a map (given or built autonomously) Map building: how to integrate sensor information and your own movement? Path planning: decide the actions to perform for reaching a target position [Many following robot slides courtesy Steffen Gutmann, SONY Labs]

Robotics Yesterday

Current Trends in Robotics Robots are moving away from factory floors to Entertainment robots Personal services Medical, surgery Industrial automation (mining, harvesting, ...) Hazardous environments (space, underwater) Military UGV, UAV, AUV Transitioning from remote control to autonomous control

Robotics Today

AI View on Mobile Robotics Sensor data Control system World model Actions

Helpmate HELPMATE is a mobile robot used in hospitals for transportation tasks. It has various on board sensors for autonomous navigation in the corridors. The main sensor for localization is a camera looking to the ceiling. It can detect the lamps on the ceiling as reference (landmark). http://statusreports-atp.nist.gov/reports/91-01- 0034.htm

Cleaning Robot - Sinas Autonomous cleaning robot with Sinas navigation system (developed by Siemens) and manifactured by Karcher. The robot is equipped with several sonar sensors, a laser range finder and a gyroscope.

Sinas Video CS225B Kurt Konolige

ROV Tiburon Underwater Robot Picture of robot ROV Tiburon for underwater archaeology (teleoperated)- used by MBARI for deep-sea research, this UAV provides autonomous hovering capabilities for the human operator.

Sojourner, First Robot on Mars The mobile robot Sojourner was used during the Pathfinder mission to explore the mars in summer 1997. It was nearly fully teleoperated from earth. However, some on board sensors allowed for obstacle detection. http://ranier.oact.hq.nasa.gov/telerobotics_page/telerobotics.shtm

The B21 Robot B21 of Real World Interface is a sophisticated mobile robot with up to three Intel Pentium processors on board. It has all different kinds of on board sensors for high performance navigation tasks.

Minerva (CMU + Univ. Bonn, 1998) Courtesy Sebastian Thrun, CMU

Pioneer 1 PIONEER 1 is a modular mobile robot offering various options like a gripper or an on board camera. It is equipped with a sophisticated navigation library developed at Stanford Research Institute (SRI) and manifactured by ActivMedia Robotics http://www.mobilerobots.com/

RoboCup – Middle Size League

Emotional Robots: Kismet Courtesy Cynthia Breazeal, MIT

Sony's AIBO Robot First model (1998) Latest model ERS 7 (2004)

RoboCup 4-legged League

The Honda Walking Robot Asimo http://www.honda.co.jp/robot/

Sony's QRIO Robot

QRIO Navigation CS225B Kurt Konolige

Darpa Grand Challenge Stanley is based on a VW Touareg R5 with 7 Pentium M computers incorporating measurements from GPS, INS, and wheel speed for pose estimation. The environment is perceived through 4 laser range finders, a radar system, a stereo camera pair, and a monocular vision system. Sensor data is processed at rates between 10 and 100 Hertz. Map and pose information are incorporated at 10 Hz, enabling Stanley to avoid collisions with obstacles in real-time while advancing along the 2005 DARPA Grand Challenge route. Standford's Stanley “racing car” Sample tracking visualization

Darpa Grand Challenge Video Courtesy Sebastian Thrun, Stanford University

Darpa Learning Applied to Ground Robotics Autonomous outdoor vehicle in unstructured environments Main sensor is stereo vision Learn models of terrain traversability Stanford Mausoleum Run Robot-view Interpretation Global Map

BigDog: Quadruped Beast of Burden Autonomous outdoor vehicle in unstructured environments Internal sensors only Video from Boston Dynamics

Trends in Robotics Research Classical AI Robotics (mid-70’s) Sense-Plan-Act Complex world model and reasoning Indoor, wheeled, static blocks world Reactive Paradigm (mid-80’s) No models: “the world is the model” Simple sense-act functions Emergent behavior Static legged motion, robot swarms, reactive Complex environments, mapping and localization, human-robot interactions Hybrid Architectures (90’s) Models at higher levels, reactive at lower levels Mid-level executive to sequence actions Challenging outdoor environments Air, water vehicles Dynamic legged motion Probabilistic Methods (mid-90’s) Uncertain sensing and acting Integration of models, sensing, acting