B659: Principles of Intelligent Robot Motion Kris Hauser TA: Mark Wilson

An intelligent robot must be able to coordinate its own motions in order to achieve certain goals

Coordinating motion demands intelligence How do cognition, learning, and reflex interact to produce intelligent behavior? How do we encode this in a robot?

Two (or Three) Core Problems  Planning: choosing present behavior to attain future goals  Sensing: making meaningful interpretations from raw data  Models are the underlying “knowledge” that are used for drawing conclusions about past, present, and future

Intelligent Robot Architecture Percept (raw sensor data) Action ?

Intelligent Robot Architecture Percept (raw sensor data) Action Sensing Planning

Intelligent Robot Architecture Percept (raw sensor data) Action Sensing algorithms Models Planning State estimation Mapping Tracking Calibration Object recognition …

Intelligent Robot Architecture Percept (raw sensor data) Action Sensing algorithms Models Real-Time Control Task PlanningMotion Planning

Goal

Planning Topics  Motion planning  Compute high-level strategies, e.g.: Geometric pathsGeometric paths Time-parameterized trajectoriesTime-parameterized trajectories Sequence of sensor-based motion commandsSequence of sensor-based motion commands  To achieve high-level goals, e.g.: Go from A to B while avoiding obstaclesGo from A to B while avoiding obstacles Assemble product PAssemble product P Build a map of environment EBuild a map of environment E Find object OFind object O

Planning Topics (cont.)  Feedback Control  Compute (or verify) a real-time strategy for responding to deviations from the desired path

Sensing Topics  State estimation  Calibrating the parameters of static models using motion Cameras and motion captureCameras and motion capture System identificationSystem identification  Filtering and estimating dynamic models State estimation and sensor fusionState estimation and sensor fusion Localization and mappingLocalization and mapping  Learning from demonstration

Modeling Topics  Rigid transformations  Kinematics and inverse kinematics  Dynamics of articulated structures  Cameras and laser rangefinders

Relationship to AI  “Sub-symbolic” intelligence  Continuous domains  Computationally complex: basic problems are in PSPACE

Goals of the Class  Present formal and practical algorithmic and mathematical tools for interpreting and synthesizing motion  Components of a general framework for designing and studying complex robotic and biological agents

This class does NOT cover…  Lagrangian mechanics  Rigid body simulation  Control theory  Numerical optimization  Computer vision  … but makes use of knowledge from these subjects

Fundamental question of motion planning  Are the two given points connected by a path? Forbidden region Feasible space

Fundamental question of motion planning  Are the two given points connected by a path? Forbidden region Feasible space e.g.: collision with obstacle lack of stability lost visibility with target …

Basic Problem  Statement: Compute a collision-free path for a rigid or articulated object among static obstacles  Inputs: Geometry of moving object and obstaclesGeometry of moving object and obstacles Kinematics of moving object (degrees of freedom)Kinematics of moving object (degrees of freedom) Initial and goal configurations (placements)Initial and goal configurations (placements)  Output: Continuous sequence of collision-free robot configurations connecting the initial and goal configurations

Why is this hard?

Tool: Configuration Space Problems: Geometric complexity Space dimensionality

Some Variants  Moving obstacles  Multiple robots  Movable objects  Assembly planning  Goal is to acquire information by sensing Model buildingModel building Object finding/trackingObject finding/tracking InspectionInspection  Nonholonomic constraints  Dynamic constraints  Stability constraints  Optimal planning  Uncertainty in model, control and sensing  Exploiting task mechanics (sensorless motions, under- actuated systems)  Physical models and deformable objects  Integration of planning and control  Integration with higher-level planning

Applications

HRP-2, AIST, Japan Humanoid Robots

Lunar Vehicle (ATHLETE, NASA/JPL)

Climbing Robot

Design for Manufacturing and Servicing General Electric General Motors

Manipulation of Deformable Objects

Assembly Sequencing

Assembly Seqencing

Cable Harness/Pipe Design

Where to move next? Map Building

Navigation Through Virtual Environments

Computer-Assisted Angiography/ Colonoscopy/ Bronchoscopy

CyberKnife (Accuray) Radiosurgical Planning

Self-Parking

Kineo Transportation of A380 Fuselage through Small Villages

Inhibitor binding to HIV protease Study of Motion of Bio-Molecules

Prerequisites  Ability and willingness to complete a significant programming project on a simulation GUI or physical robot  Interest in reading and discussing research papers each week  Subjects: linear algebra*, multivariable calculus, geometry, probability  Basic knowledge and taste for geometry, mathematical analysis, and algorithms

Book  Principles of Robot Motion (Choset, Hutchinson, Kantor, Burgard, Kavraki, and Thrun)

Grading  Participation: read readings, attend class prepared to discuss readings  Presentation(s): read and understand research paper, and make 20 min PPT presentation to class  Semester-long project

Semester Project  Topic of your choosing, advised and approved by instructor  Groups of 1-3 students  Schedule Proposal (Feb.)Proposal (Feb.) Mid term report / discussion (March)Mid term report / discussion (March) Final presentation (end of April)Final presentation (end of April)

Project Ideas  Robot chess  Finding and tracking people indoors  UI for assistive robot arms  Analysis of human observation data (Prof. Yu)  Outdoor vehicle navigation (Prof. Johnson)  Motion in social contexts (Profs. Scheutz and Sabanovic)

Class Website  Please visit website for course policies, important announcements, and detailed schedule:  Or from click ‘Teaching’