Applied soft computing strategies for autonomous field robotics

This chapter addresses computing strategies designed to enable field mobile robots to execute tasks requiring effective autonomous traversal of natural outdoor terrain. The primary focus is on computer vision-based perception and autonomous control. Hard computing methods are combined with applied soft computing strategies in the context of three case studies associated with real-world robotics tasks including planetary surface exploration and land survey or reconnaissance. Each case study covers strategies implemented on wheeled robot research prototypes designed for field operations.

[1]  C. S. G. Lee,et al.  Robotics: Control, Sensing, Vision, and Intelligence , 1987 .

[2]  Terrence J. Sejnowski,et al.  The Computational Brain , 1996, Artif. Intell..

[3]  Gaurav S. Sukhatme,et al.  Robust localization using relative and absolute position estimates , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[4]  J. Daugman Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[5]  Peter N. Belhumeur,et al.  A binocular stereo algorithm for reconstructing sloping, creased, and broken surfaces in the presence of half-occlusion , 1993, 1993 (4th) International Conference on Computer Vision.

[6]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[7]  Homayoun Seraji,et al.  A rule-based fuzzy traversability index for mobile robot navigation , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[8]  Michael R. M. Jenkin,et al.  Computational principles of mobile robotics , 2000 .

[9]  T.T. Nguyen,et al.  Experiences with operations and autonomy of the Mars Pathfinder Microrover , 1998, 1998 IEEE Aerospace Conference Proceedings (Cat. No.98TH8339).

[10]  Roberto Manduchi,et al.  Terrain perception for DEMO III , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[11]  Ashitey Trebi-Ollennu,et al.  Rover localization results for the FIDO rover , 2001, SPIE Optics East.

[12]  M. Kaplan Modern Spacecraft Dynamics and Control , 1976 .

[13]  Barbara Hayes-Roth,et al.  Intelligent Control , 1994, Artif. Intell..

[14]  Brian H. Wilcox,et al.  Non-geometric hazard detection for a Mars microrover , 1994 .

[15]  J. S. Bay,et al.  A fuzzy logic solution for navigation of an autonomous subsurface planetary exploration robot , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[16]  John M. Dolan,et al.  Adaptive fuzzy throttle control for an all-terrain vehicle , 2001 .

[17]  Larry H. Matthies,et al.  Stereo vision for planetary rovers: Stochastic modeling to near real-time implementation , 1991, Optics & Photonics.

[18]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[19]  Terrance L. Huntsberger Comparison of techniques for disparate sensor fusion , 1991, Other Conferences.

[20]  S. Zucker,et al.  Endstopped neurons in the visual cortex as a substrate for calculating curvature , 1987, Nature.

[21]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[22]  Robert L. Cannon,et al.  Iterative fuzzy image segmentation , 1985, Pattern Recognit..

[23]  HANI HAGRAS,et al.  Outdoor mobile robot learning and adaptation , 2001, IEEE Robotics Autom. Mag..

[24]  I. Daubechies,et al.  Multiresolution analysis, wavelets and fast algorithms on an interval , 1993 .

[25]  Terrance L. Huntsberger,et al.  PARALLEL SELF-ORGANIZING FEATURE MAPS FOR UNSUPERVISED PATTERN RECOGNITION , 1990 .

[26]  Hugh F. Durrant-Whyte,et al.  Inertial navigation systems for mobile robots , 1995, IEEE Trans. Robotics Autom..

[27]  Sebastian Thrun,et al.  Probabilistic Algorithms in Robotics , 2000, AI Mag..

[28]  Alessandro Saffiotti,et al.  Fuzzy Logic Techniques for Autonomous Vehicle Navigation , 2001 .

[29]  D. Hubel,et al.  Shape and arrangement of columns in cat's striate cortex , 1963, The Journal of physiology.

[30]  T D Gillespie,et al.  Fundamentals of Vehicle Dynamics , 1992 .

[31]  Paul S. Schenker,et al.  Reconfigurable robots for all-terrain exploration , 2000, SPIE Optics East.

[32]  Roberto Manduchi,et al.  Ladar-Based Discrimination of Grass from Obstacles for Autonomous Navigation , 2000, ISER.

[33]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Sun-Yuan Kung,et al.  Principal Component Neural Networks: Theory and Applications , 1996 .

[35]  Edward Tunstel,et al.  Approximate reasoning for safety and survivability of planetary rovers , 2003, Fuzzy Sets Syst..

[36]  Clark F. Olson,et al.  Stereo ego-motion improvements for robust rover navigation , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[37]  Paul S. Schenker,et al.  Improved Rover State Estimation in Challenging Terrain , 1999, Auton. Robots.

[38]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[39]  James C. Bezdek,et al.  Generalized clustering networks and Kohonen's self-organizing scheme , 1993, IEEE Trans. Neural Networks.