ABSTRACT
Algorithms that control the computational processes relating sensors and actuators are indispensable for robot navigation and the perception of the world in which they move. Therefore, a deep understanding of how algorithms work to achieve this control is essential for the development of efficient and usable robots in a broad field of applications.
TABLE OF CONTENTS
chapter |8 pages
Meso-Scale Self-Assembly
David H. Gracias, Harvard University, Cambridge, MA Insung Choi, Harvard University, Cambridge, MA Marcus Week, Harvard University, Cambridge, MA George M. Whitesides, Harvard University, Cambridge, MA
part |1 pages
Controlled Module Density Helps Reconfiguration Planning
part |2 pages
References
chapter |14 pages
Positioning Symmetric and Non-Symmetric Parts using Radial and Constant Force Fields
Florent Lamiraux, LAAS-CNRS, Toulouse, France Lydia E. Kavraki, Rice University, Houston, TX
chapter |8 pages
Complete Distributed Coverage of Rectilinear Environments
Zack J. Butler, Carnegie Mellon University, Pittsburgh, PA Alfred A. Rizzi, Carnegie Mellon University, Pittsburgh, PA Ralph L. Hollis, Carnegie Mellon University, Pittsburgh, PA
part |2 pages
Closed-Loop Distributed Manipulation Using Discrete Actuator Arrays
chapter |10 pages
Kinematic Tolerance Analysis with Configuration Spaces
Leo Joskowicz, The Hebrew University, Jerusalem, Israel Elisha Sacks, Purdue University, West Lafayette, IN
chapter |4 pages
Deformable Free Space Tilings for Kinetic Collision Detection
Pankaj K. Agarwal, Duke University, Durham, NC Julien Basch, Stanford University, Stanford, CA Leonidas J. Guibas, Stanford University, Stanford, CA John Hershberger, Mentor Graphics Corp., Wilsonville, OR
chapter |3 pages
Real-time Global Deformations
Yan Zhuang, University of California, Berkeley, CA John Canny, University of California, Berkeley, CA
chapter |16 pages
Motion Planning for Kinematic Stratified Systems with Application to Quasi-Static Legged Locomotion and Finger Gaiting
Bill Goodwine, University of Notre Dame, Notre Dame, IN Joel W. Burdick, California Institute of Technology, Pasadena, CA
part |2 pages
References
chapter |11 pages
Manipulation of Pose Distributions
Mark Moll, Carnegie Mellon University, Pittsburgh, PA Michael A. Erdmann, Carnegie Mellon University, Pittsburgh, PA
chapter |14 pages
Image Guided Surgery
Eric Grimson, MIT, Cambridge, MA Michael Leventon, MIT, Cambridge, MA Liana Lorigo, MIT, Cambridge, MA Tina Kapur, Visualization Technology Incorporated, Wilmington, MA Olivier Faugeras, MIT, Cambridge, MA
chapter |4 pages
Pulling Motion Based Tactile Sensing
Makoto Kaneko, Hiroshima University, Higashi-Hiroshima, Japan Toshio Tsuji, Hiroshima University, Higashi-Hiroshima, Japan
part 6|2 pages
Conclusions
chapter |12 pages
Compensatory Grasping with the Parallel Jaw Gripper
Tao Zhang, University of California, Berkeley, Berkeley, CA Gordon Smith, University of California, Berkeley, Berkeley, CA Ken Goldberg, University of California, Berkeley, Berkeley, CA
chapter |8 pages
Optimal Planning for Coordinated Vehicles
Antonio Bicchi Centro “E. Piaggio, ” University of Pisa, Pisa, Italy Lucia Pallottino Centro UE. Piaggio, ” University of Pisa, Pisa, Italy
part |2 pages
Acknowledgments
chapter |14 pages
An Efficient Approximation Algorithm for Weighted Region Shortest Path Problem
John Reif, Duke University, Durham, NC Zheng Sun, Duke University, Durham, NC 1 Introduction
chapter |16 pages
Synthesis and Regulation of Cyclic Behaviors
E. Klavins University of Michigan, Ann Arbor, MI D.E. Koditschek University of Michigan, Ann Arbor, MI R. Ghrist Georgia Institute of Technology, Atlanta, GA
chapter |6 pages
A Framework for Steering Dynamic Robotic Locomotion Systems J. P. Ostrowski and K. A . M clsaac
James P. Ostrowski, University of Pennsylvania, Philadelphia, PA Kenneth A. Mclsaac, University of Pennsylvania, Philadelphia, PA
chapter |9 pages
A Kinematics-Based Probabilistic Roadmap Method for Closed Chain Systems
Li Han, Texas A Nancy M. Amato, Texas A
part |2 pages
Randomized Kinodynamic Motion Planning with Moving Obstacles
chapter |10 pages
to be compu-
u is a vector in !Rm= !Rn-k_ Recipro- be rewritten into k m in- of the form Fi ( s, s) of the form Gi(q, q, ij) that will be useful later in the paper: to time, and u E The set Nonholonomic car-like robot. Consider a car A mod- S and are the robot's state space and control space, x, y) be the posi- state at time t and a control tion of the midpoint R between A's rear wheels and of !Rn and !Rm. both nonholonomic and the same form as Equation (1). This refor- to defining the state of A to be of the form Fi(q,q) = = ... ,k, and choosing the vector (v, ¢), where q the and v and the car's and
chapter |12 pages
On Random Sampling in Contact Configuration Space
Xuerong Ji, University o Jing Xiao, University o
part |2 pages
Method CF dof time(s) CF dof time(s) Direct {f-f}, Figure 12(a) 3 0.23 {e-f}, Figure 12(b) 4 0.25 Direct {v-f}, Figure 12(c) 5 0.45 {e-e-c}, Figure 12(d) 5 0.45 Direct {f-f, f-f}, Figure 13(a) 16.7 {e-f, f-f}, Figure 13(b) 13.5 Hybrid {e-f, f-f}, Figure 13(c) 23.3 or 3.4 {e-e-c, f-f}, Figure 13(d) 23.7 or 3.8 Hybrid {e-f, e-f}, Figure 13(e) 2 49.5 or 54.6 {e-f, f-e}, Figure 13(f) 2 61.1 or 56.1 Hybrid {v-f, e-f}, Figure 13(g) 3 50.4 or 50.9 {v-f, v-f}, Figure 13(h) 4 34.7 or 40.4
chapter |3 pages
Randomized Path Planning for a Rigid Body Based on Hardware Accelerated Voronoi Sampling
Charles Pisula, University of North Carolina, Chapel Hill, NC Kenneth Hoff III, University of North Carolina, Chapel Hill, NC Ming C. Lin, University of North Carolina, Chapel Hill, NC Dinesh Manocha, University of North Carolina, Chapel Hill, NC
part 3|2 pages
Path Planning Based on Discretized
chapter |5 pages
Rapidly-Exploring Random Trees: Progress and Prospects
Steven M. LaValle, Iowa State University, A James J. Kuffner, Jr., University of Tokyo, Tokyo, Japan
part X|1 pages
is
part |2 pages
Encoders for Spherical Motion Using Discrete Optical Sensors
chapter |12 pages
Notes on Visibility Roadmaps and Path Planning
J.-P. Laumond, LAAS-CNRS, Toulouse, France T. Simeon, LAAS-CNRS, Toulouse, France
chapter |12 pages
AutoBalancer: An Online Dynamic Balance Compensation Scheme for Humanoid Robots
Satoshi Kagami, Fumio Kanehiro, Yukiharu Tamiya, Masayuki Inaba,
chapter |7 pages
Coupled Oscillators for Legged Robots
Matthew D. Berkemeier, Utah State University, Logan, UT
part |1 pages
References
chapter |3 pages
Reliable Mobile Robot Navigation From Unreliable Visual Cues
Amy J. Briggs, Middlebury College, Middlebury, VT Daniel Scharstein, Middlebury College, Middlebury, VT Stephen D. Abbott, Middlebury College, Middlebury, VT
part 3|2 pages
2 Exploration and Navigation
chapter |6 pages
for a fixed p. An affine transformation yields: A(L(x, y)) = L(ax+by+c, dx+ey+ f) = sp(ax+by+c). = get sp(ax
Sampled at y sp(ax + t). Thus, the problem of finding an affine transformation of the two-dimensional pattern L has been reduced to finding a translation t of the one- dimensional pattern 5 The Landmark Recognition and w = 50. The length of and w = 45. each scanline 400. The top two patterns are locally
chapter |14 pages
Toward Real-Time Motion Planning in Changing Environments
Peter Leven, University of Illinois, Urbana, IL Seth Hutchinson, University of Illinois, Urbana, IL
chapter |14 pages
Graphical Construction of Time Optimal Trajectories for Differential Drive Robots
Devin J. Balkcom, Carnegie Mellon University, Pittsburgh, PA Matthew T. Mason, Carnegie Mellon University, Pittsburgh, PA
