Intentional Control for Planetary Rover SRR

Intentional behavior is a basic property of intelligence, and it incorporates the cyclic operation of prediction, testing by action, sensing, perceiving and assimilating the experienced features. Intentional neurodynamic principles are applied for on-line processing of multisensory inputs and for the generation of dynamic behavior using the SRR (Sample Return Rover) platform at the indoor facility of the Planetary Robotics Laboratory, Jet Propulsion Laboratory. The studied sensory modalities include CMOS camera vision, orientation based on an internal motion unit and accelerometer signals. The control architecture employs a biologically inspired dynamic neural network operating on the principle of chaotic neural dynamics manifesting intentionality in the style of mammalian brains. Learning is based on Hebbian rules coupled with reinforcement. The central issue of this work is to study how the developed control system builds associations between the sensory modalities to achieve robust autonomous action selection. The proposed system builds such associations in a self-organized way and it is called Self-Organized Development of Autonomous Adaptive Systems (SODAS). This system operates autonomously, without the need for human intervention, which is a potentially very beneficial feature in challenging environments, such as encountered in space explorations at remote planetary environments. The experiments illustrate obstacle avoidance combined with goal-oriented navigation by the SRR robot using SODAS control principles.

[1]  Maja J. Mataric,et al.  Integration of representation into goal-driven behavior-based robots , 1992, IEEE Trans. Robotics Autom..

[2]  Robert Kozma,et al.  Spatial navigation model based on chaotic attractor networks , 2004, Connect. Sci..

[3]  Robert Kozma,et al.  Chaotic Resonance - Methods and Applications for Robust Classification of noisy and Variable Patterns , 2001, Int. J. Bifurc. Chaos.

[4]  R. Jindra Mass action in the nervous system W. J. Freeman, Academic Press, New York (1975), 489 pp., (hard covers). $34.50 , 1976, Neuroscience.

[5]  L. Brakel A Universe of Consciousness: How Matter Becomes Imagination , 2001 .

[6]  Guido E. Vallejos Mindware: An introduction to the philosophy of cognitive science , 2010 .

[7]  Maja J. Matarić,et al.  Navigating with a rat brain: a neurobiologically-inspired model for robot spatial representation , 1991 .

[8]  Ricardo Gutierrez-Osuna,et al.  Contrast enhancement and background suppression of chemosensor array patterns with the KIII model , 2006, Int. J. Intell. Syst..

[9]  Frank Pasemann,et al.  Dynamical Neural Schmitt Trigger for Robot Control , 2002, ICANN.

[10]  Robert Kozma,et al.  Implementing reinforcement learning in the chaotic KIV model using mobile robot AIBO , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[11]  J. O’Keefe,et al.  Phase relationship between hippocampal place units and the EEG theta rhythm , 1993, Hippocampus.

[12]  Hrand Aghazarian,et al.  Learning to behave: adaptive behavior for planetary surface rovers , 2004 .

[13]  Péter Érdi,et al.  The KIV model - nonlinear spatio-temporal dynamics of the primordial vertebrate forebrain , 2003, Neurocomputing.

[14]  Allen M. Waxman,et al.  Mobile robot visual mapping and localization: A view-based neurocomputational architecture that emulates hippocampal place learning , 1994, Neural Networks.

[15]  Robert Kozma,et al.  Emergence of un-correlated common-mode oscillations in the sensory cortex , 2001, Neurocomputing.

[16]  T. S. Collett,et al.  Landmark maps for honeybees , 1987, Biological Cybernetics.

[17]  Robert Kozma,et al.  Basic principles of the KIV model and its application to the navigation problem. , 2003, Journal of integrative neuroscience.

[18]  Paolo Pirjanian,et al.  Robotic outposts as precursors to a manned Mars habitat , 2001 .

[19]  Maja J. Matari,et al.  Behavior-based Control: Examples from Navigation, Learning, and Group Behavior , 1997 .

[20]  W. Freeman,et al.  Restoring to cognition the forgotten primacy of action, intention and emotion , 1999 .

[21]  Homayoun Seraji,et al.  Behavior-based robot navigation on challenging terrain: A fuzzy logic approach , 2002, IEEE Trans. Robotics Autom..

[22]  Robert Kozma,et al.  Nonlinear Neurodynamics Tool for System Analysis and Application for Time Series Prediction , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[23]  Jean-Arcady Meyer,et al.  BIOLOGICALLY BASED ARTIFICIAL NAVIGATION SYSTEMS: REVIEW AND PROSPECTS , 1997, Progress in Neurobiology.

[24]  L. F. Abbott,et al.  A Model of Spatial Map Formation in the Hippocampus of the Rat , 1999, Neural Computation.

[25]  Paul F. M. J. Verschure,et al.  A real-world rational agent: unifying old and new AI , 2003, Cogn. Sci..

[26]  Pattie Maes,et al.  Designing autonomous agents: Theory and practice from biology to engineering and back , 1990, Robotics Auton. Syst..

[27]  Walter J. Freeman,et al.  Neurodynamics: An Exploration in Mesoscopic Brain Dynamics , 2000, Perspectives in Neural Computing.

[28]  Donald O. Walter,et al.  Mass action in the nervous system , 1975 .

[29]  Terrance L. Huntsberger,et al.  BISMARC: a biologically inspired system for map-based autonomous rover control , 1998, Neural Networks.

[30]  Robert Kozma,et al.  Navigation and Cognitive Map Formation Using Aperiodic Neurodynamics , 2004 .

[31]  Benjamin Kuipers,et al.  The Spatial Semantic Hierarchy , 2000, Artif. Intell..

[32]  Terrance L. Huntsberger,et al.  Autonomous multirover system for complex planetary surface retrieval operations , 1997, Other Conferences.

[33]  Robert Kozma,et al.  Chaotic neurodynamics for autonomous agents , 2005, IEEE Transactions on Neural Networks.

[34]  Stefan Schaal,et al.  Navigation and Cognitive Map Formation Using Aperiodic Neurodynamics , 2004 .

[35]  Ben J. A. Kröse,et al.  Distributed adaptive control: The self-organization of structured behavior , 1992, Robotics Auton. Syst..

[36]  Toshio Fukuda,et al.  Intentional dynamic systems: Fundamental concepts and applications , 2006, Int. J. Intell. Syst..

[37]  David Kortenkamp,et al.  Topological Mapping for Mobile Robots Using a Combination of Sonar and Vision Sensing , 1994, AAAI.

[38]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[39]  H. Aghazarian,et al.  Computational Aspects of Cognition and Consciousness in Intelligent Devices , 2007, IEEE Computational Intelligence Magazine.

[40]  Robert Kozma,et al.  Navigation in a challenging Martian environment using multi-sensory fusion in KIV model , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[41]  Maja J. Mataric,et al.  Behaviour-based control: examples from navigation, learning, and group behaviour , 1997, J. Exp. Theor. Artif. Intell..

[42]  Olaf Sporns,et al.  Plasticity in Value Systems and its Role in Adaptive Behavior , 2000, Adapt. Behav..

[43]  Paul F. M. J. Verschure,et al.  A real-world rational agent: unifying old and new AI , 2003 .

[44]  Paul S. Schenker,et al.  CAMPOUT: a control architecture for multirobot planetary outposts , 2000, SPIE Optics East.

[45]  Arvin Agah,et al.  Phylogenetic and Ontogenetic Learning in a Colony of Interacting Robots , 1997, Auton. Robots.

[46]  Edward Tunstel Ethology as an Inspiration for Adaptive Behavior Synthesis in Autonomous Planetary Rovers , 2001, Auton. Robots.

[47]  Walter J. Freeman,et al.  Optimization of olfactory model in software to give 1/f power spectra reveals numerical instabilities in solutions governed by aperiodic (chaotic) attractors , 1998, Neural Networks.