Integration of Representation Into Goal-Driven Behavior-Based Robots By Maja J. Mataric Presented by Murali Kiran. M.

Slides:



Advertisements
Similar presentations
An Extension to the Dynamic Window Approach
Advertisements

Motion Planning for Point Robots CS 659 Kris Hauser.
MICHAEL MILFORD, DAVID PRASSER, AND GORDON WYETH FOLAMI ALAMUDUN GRADUATE STUDENT COMPUTER SCIENCE & ENGINEERING TEXAS A&M UNIVERSITY RatSLAM on the Edge:
ROBOT LOCALISATION & MAPPING: MAPPING & LIDAR By James Mead.
University of Amsterdam Search, Navigate, and Actuate - Quantitative Navigation Arnoud Visser 1 Search, Navigate, and Actuate Quantative Navigation.
The Voronoi Diagram David Johnson. Voronoi Diagram Creates a roadmap that maximizes clearance –Can be difficult to compute –We saw an approximation in.
Motion planning, control and obstacle avoidance D. Calisi.
Randomized Motion Planning for Car-like Robots with C-PRM Guang Song and Nancy M. Amato Department of Computer Science Texas A&M University College Station,
A Robotic Wheelchair for Crowded Public Environments Choi Jung-Yi EE887 Special Topics in Robotics Paper Review E. Prassler, J. Scholz, and.
Real-Time Tracking of an Unpredictable Target Amidst Unknown Obstacles Cheng-Yu Lee Hector Gonzalez-Baños* Jean-Claude Latombe Computer Science Department.
1 Mobile Sensor Network Deployment using Potential Fields : A Distributed, Scalable Solution to the Area Coverage Problem Andrew Howard, Maja J Mataric´,
Robotic Mapping: A Survey Sebastian Thrun, 2002 Presentation by David Black-Schaffer and Kristof Richmond.
ECE 4340/7340 Exam #2 Review Winter Sensing and Perception CMUcam and image representation (RGB, YUV) Percept; logical sensors Logical redundancy.
Shirokuro : A Backtracking Approach Benjamin Bush Faculty Advisors: Dr. Russ Abbott, Dr. Gary Brookfield Department of Computer Science, Department of.
Search-based Path Planning with Homotopy Class Constraints Subhrajit Bhattacharya Vijay Kumar Maxim Likhachev University of Pennsylvania GRASP L ABORATORY.
1/22 Robot Formations Using Only Local Sensing And Control Jakob Fredslund, Maja J Mataric {jakobf, Interaction Lab, University.
1 Target Finding. 2 Example robot’s visibility region hiding region 1 cleared region robot.
CS 326A: Motion Planning Criticality-Based Motion Planning: Target Finding.
CS 326 A: Motion Planning robotics.stanford.edu/~latombe/cs326/2003/index.htm Configuration Space – Basic Path-Planning Methods.
Integration of Representation Into Goal- Driven Behavior-Based Robots By Dr. Maja J. Mataric` Presented by Andy Klempau.
Study on Mobile Robot Navigation Techniques Presenter: 林易增 2008/8/26.
CS 326A: Motion Planning Basic Motion Planning for a Point Robot.
Planetary Surface Robotics ENAE 788U, Spring 2005 U N I V E R S I T Y O F MARYLAND Lecture 8 Mapping 5 April, 2005.
Navigation and Metric Path Planning
Bayesian Filtering for Location Estimation D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello Presented by: Honggang Zhang.
June 12, 2001 Jeong-Su Han An Autonomous Vehicle for People with Motor Disabilities by G. Bourhis, O.Horn, O.Habert and A. Pruski Paper Review.
Lab 3 How’d it go?.
Teaching Deliberative Navigation Using the LEGO RCX and Standard LEGO Components Gary R. Mayer, Dr. Jerry Weinberg, Dr. Xudong Yu
Exposure In Wireless Ad-Hoc Sensor Networks Seapahn Meguerdichian Computer Science Department University of California, Los Angeles Farinaz Koushanfar.
Exposure In Wireless Ad-Hoc Sensor Networks Seapahn Meguerdichian Computer Science Department University of California, Los Angeles Farinaz Koushanfar.
Nuttapon Boonpinon Advisor Dr. Attawith Sudsang Department of Computer Engineering,Chulalongkorn University Pattern Formation for Heterogeneous.
Mapping and Localization for Robots The Occupancy Grid Approach.
© Manfred Huber Autonomous Robots Robot Path Planning.
Pattern Similarity and Storage Capacity of Hopfield Network Suman K Manandhar Prof. Ramakoti Sadananda Computer Science and Information Management AIT.
The Eos-Explorer CHENRAN YE IMDE ECE 4665/5666 Fall 2011.
Using Soar for an indoor robotic search mission Scott Hanford Penn State University Applied Research Lab 1.
Visibility Graph. Voronoi Diagram Control is easy: stay equidistant away from closest obstacles.
University of Amsterdam Search, Navigate, and Actuate - Qualitative Navigation Arnoud Visser 1 Search, Navigate, and Actuate Qualitative Navigation.
USC Search Space Properties for Pipelined FPGA Applications University of Southern California Information Sciences Institute Heidi Ziegler, Mary Hall,
Wandering Standpoint Algorithm. Wandering Standpoint Algorithm for local path planning Description: –Local path planning algorithm. Required: –Local distance.
1 Distributed and Optimal Motion Planning for Multiple Mobile Robots Yi Guo and Lynne Parker Center for Engineering Science Advanced Research Computer.
A Multidisciplinary Approach for Using Robotics in Engineering Education Jerry Weinberg Gary Mayer Department of Computer Science Southern Illinois University.
9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning1 Part II Chapter 9: Topological Path Planning.
Using RouteGraphs as an Appropriate Data Structure for Navigational Tasks SFB/IQN-Kolloquium Christian Mandel, A1-[RoboMap] Overview Goal scenario.
Topological Path Planning JBNU, Division of Computer Science and Engineering Parallel Computing Lab Jonghwi Kim Introduction to AI Robots Chapter 9.
Turning Autonomous Navigation and Mapping Using Monocular Low-Resolution Grayscale Vision VIDYA MURALI AND STAN BIRCHFIELD CLEMSON UNIVERSITY ABSTRACT.
Efficient Computing k-Coverage Paths in Multihop Wireless Sensor Networks XuFei Mao, ShaoJie Tang, and Xiang-Yang Li Dept. of Computer Science, Illinois.
Robotics Club: 5:30 this evening
Chapter 10. The Explorer System in Cognitive Systems, Christensen et al. Course: Robots Learning from Humans On, Kyoung-Woon Biointelligence Laboratory.
Lecture 5: Grid-based Navigation for Mobile Robots.
Probabilistic Robotics
Motion Planning Howie CHoset. Assign HW Algorithms –Start-Goal Methods –Map-Based Approaches –Cellular Decompositions.
Coverage Problems in Wireless Ad-hoc Sensor Networks Seapahn Meguerdichian 1 Farinaz Koushanfar 2 Miodrag Potkonjak 1 Mani Srivastava 2 University of California,
1 Dynamic Speed and Sensor Rate Adjustment for Mobile Robotic Systems Ala’ Qadi, Steve Goddard University of Nebraska-Lincoln Computer Science and Engineering.
Ghislain Fouodji Tasse Supervisor: Dr. Karen Bradshaw Computer Science Department Rhodes University 04 August 2009.
Learning Roomba Module 5 - Localization. Outline What is Localization? Why is Localization important? Why is Localization hard? Some Approaches Using.
Repairing Sensor Network Using Mobile Robots Y. Mei, C. Xian, S. Das, Y. C. Hu and Y. H. Lu Purdue University, West Lafayette ICDCS 2006 Speaker : Shih-Yun.
Heterogeneous Teams of Modular Robots for Mapping and Exploration by Grabowski et. al.
Autonomous Robots Robot Path Planning (2) © Manfred Huber 2008.
Power-Aware Topology Control for Wireless Ad-Hoc Networks Wonseok Baek and C.-C. Jay Kuo Department of Electrical Engineering University of Southern California.
How do I get there? Roadmap Methods Visibility Graph Voronoid Diagram.
COGNITIVE APPROACH TO ROBOT SPATIAL MAPPING
Example robot cleared region robot’s visibility region hiding region 2
Schedule for next 2 weeks
Spatial Semantic Hierarchy (SSH)
Part II Chapter 9: Topological Path Planning
Principles of GIS Geocomputation – Part II Shaowen Wang
Today: Localization & Navigation Wednesday: Image Processing
Planning.
Presentation transcript:

Integration of Representation Into Goal-Driven Behavior-Based Robots By Maja J. Mataric Presented by Murali Kiran. M

About the Author Associate Professor Computer Science Department and Neuroscience Program Director, Center for Robotics and Embedded Systems (CRES) Co-Director, Robotics Research Lab President-Elect, Academic Senate Chair, VSoE Women in Science and Engineering (WiSE) Viterbi School of Engineering (VSoE), University of Southern California Associate Professor Computer Science Department and Neuroscience Program Director, Center for Robotics and Embedded Systems (CRES) Co-Director, Robotics Research Lab President-Elect, Academic Senate Chair, VSoE Women in Science and Engineering (WiSE) Viterbi School of Engineering (VSoE), University of Southern California Computer Science DepartmentNeuroscience ProgramCenter for Robotics and Embedded Systems (CRES)Robotics Research LabAcademic SenateVSoEWomen in Science and Engineering (WiSE) Viterbi School of Engineering (VSoE)University of Southern California Computer Science DepartmentNeuroscience ProgramCenter for Robotics and Embedded Systems (CRES)Robotics Research LabAcademic SenateVSoEWomen in Science and Engineering (WiSE) Viterbi School of Engineering (VSoE)University of Southern California Homepage: Homepage:

Contact Address Computer Science Department University of Southern California Office: Ronald Tutor Hall (RTH) 407 Mailing address: Henry Salvatori, Mailcode West 37th Place Los Angeles, CA USA Computer Science Department University of Southern California Office: Ronald Tutor Hall (RTH) 407 Mailing address: Henry Salvatori, Mailcode West 37th Place Los Angeles, CA USA Computer Science Department University of Southern California Computer Science Department University of Southern California

Task to perform Explore an office environment Explore an office environment Construct and maintain a map based on the landmarks it discovers. Construct and maintain a map based on the landmarks it discovers.

TOTO Omni directional three wheeled base. Omni directional three wheeled base. Twelve ultrasonic ranging sensors Twelve ultrasonic ranging sensors Flux gate compass Flux gate compass

TOTO

Competencies Basic navigation Basic navigation Landmark detection Landmark detection Map-related computation Map-related computation

Robot Behavior Stroll Stroll Avoid Avoid Align Align Correct Correct

Stroll

Avoid

Align

Correct

Schematic Diagram

Landmark Detection

Acquiring world model Acquiring world model Sensors Sensors Perceptual limitations Perceptual limitations Sensor noise Sensor noise Drift or slippage Drift or slippage Complexity and dynamics Complexity and dynamics

Fundamental paradigm Grid-based (metric) paradigm Grid-based (metric) paradigm Topological paradigm Topological paradigm

Grid-based paradigm Represents environment by evenly spaced grids. Represents environment by evenly spaced grids. Grid cell may represent an obstacle in the corresponding region of the environent. Grid cell may represent an obstacle in the corresponding region of the environent.

Topological paradigm Represents robots environments by graphs Represents robots environments by graphs Nodes in such graphs represent distinct places or landmarks. Nodes in such graphs represent distinct places or landmarks. They are connected by arcs if they have a direct path between them. They are connected by arcs if they have a direct path between them. Topological maps are build over Grid based maps Topological maps are build over Grid based maps We are concerned about the topological paradigm We are concerned about the topological paradigm

Differences

Constructing topological maps Thresholding Thresholding Voronoi Diagram Voronoi Diagram Critical points Critical points Critical lines Critical lines Topological graph Topological graph

Thresholding Cells whose occupancy value is below the threshold are considered as free space. Cells whose occupancy value is below the threshold are considered as free space. Free space denoted by  C Free space denoted by  C All other points are considered as occupied. All other points are considered as occupied. _ They are denoted by  C. They are denoted by  C.

Voronoi Diagram (x,y) is a point in C. (x,y) is a point in C. Nearest points to (x,y) in the occupied space are called basic points. Nearest points to (x,y) in the occupied space are called basic points. Clearence is the distance between the basic points and (x,y). Clearence is the distance between the basic points and (x,y). Voronoi diagram is the set of all points in the free space that have atleast two different basic points. Voronoi diagram is the set of all points in the free space that have atleast two different basic points.

Critical points Critical points (x,y) are points on the Voronoi diagram that minimize clearance locally. Each critical point (x,y) has the following two properties: (a) it is part of the Voronoi diagram. (b) the clearance of all points in an "neighborhood of (x,y) is not smaller.

Critical Lines Critical lines are obtained by connecting each critical point with its basis points.

Topological graph The partitioning is mapped into an isomorphic map. The partitioning is mapped into an isomorphic map.

The Tuple The Tuple

Graph Representation

Mapping Algorithm

Conclusion