Behavioral models of the praying mantis as a basis for robotic behavior

Abstract Formal models of animal sensorimotor behavior can provide effective methods for generating robotic intelligence. In this article we describe how schema-theoretic models of the praying mantis derived from behavioral and neuroscientific data can be implemented on a hexapod robot equipped with a real time color vision system. This implementation incorporates a wide range of behaviors, including obstacle avoidance, prey acquisition, predator avoidance, mating, and chantlitaxia behaviors that can provide guidance to neuroscientists, ethologists, and roboticists alike. The goals of this study are threefold: to provide an understanding and means by which fielded robotic systems are not competing with other agents that are more effective at their designated task; to permit them to be successful competitors within the ecological system and capable of displacing less efficient agents; and that they are ecologically sensitive so that agent–environment dynamics are well-modeled and as predictable as possible whenever new robotic technology is introduced.

[1]  Leslie Pack Kaelbling,et al.  Ecological Robotics: Controlling Behavior with Optical Flow , 1995 .

[2]  R. Beer,et al.  Intelligence as Adaptive Behavior: An Experiment in Computational Neuroethology , 1990 .

[3]  Michael A. Arbib,et al.  Learning to Detour , 1995, Adapt. Behav..

[4]  Russell D. Fernald,et al.  Neuroethology: An introduction to the neurophysiological fundamentals of behavior, Jörg‐Peter Ewert, Springer‐Verlag, Berlin, Heidelberg, New York, 1980, 342 pp. Price: $27.00 , 1981 .

[5]  Tucker R. Balch,et al.  Communication in reactive multiagent robotic systems , 1995, Auton. Robots.

[6]  Ronald C. Arkin,et al.  Dimensions of communication and social organization in multi-agent robotic systems , 1993 .

[7]  David P. Miller.  Experiences Looking into Niches , 1995 .

[8]  U. Neisser.  Cognitive Psychology. (Book Reviews: Cognition and Reality. Principles and Implications of Cognitive Psychology) , 1976 .

[9]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[10]  D. Ingle.  4 – Spatial Vision in Anurans , 1976 .

[11]  Michael A. Arbib,et al.  Schema theory , 1998 .

[12]  Michael A. Arbib,et al.  Web Simulation Of Brain Models , 1999 .

[13]  M. S. Mayzner,et al.  Cognition And Reality , 1976 .

[14]  J. Ewert.  Tectal Mechanisms That Underlie Prey-Catching and Avoidance Behaviors in Toads , 1984 .

[15]  Michael A. Arbib,et al.  Perceptual Structures and Distributed Motor Control , 1981 .

[16]  M. Arbib,et al.  A neural model of interactions subserving prey-predator discrimination and size preference in anuran amphibia. , 1985, Journal of theoretical biology.

[17]  Pattie Maes,et al.  The Dynamics of Action Selection , 1989, IJCAI.

[18]  U. Neisser.  Cognition and reality: principles and implications , 1976 .

[19]  R. Didday.  A model of visuomotor mechanisms in the frog optic tectum , 1976 .

[20]  J. Ewert.  Neuroethology of releasing mechanisms: Prey-catching in toads , 1987, Behavioral and Brain Sciences.

[21]  Michael A. Arbib,et al.  A formal model of computation for sensory-based robotics , 1989, IEEE Trans. Robotics Autom..

[22]  Tucker R. Balch,et al.  Io, Ganymede, and Callisto A Multiagent Robot Trash-Collecting Team , 1995, AI Mag..

[23]  Alfredo Weitzenfeld,et al.  NSL-Neural Simulation Language , 1995, IWANN.

[24]  Francisco Cervantes-Pérez,et al.  Schema Theory as a Common Language to Study Sensori-Motor Coordination , 1989 .

[25]  F. Cervantes-Pérez,et al.  Modulation of prey-catching behavior in toads: data and modeling , 1991 .

[26]  Jörg-Peter Ewert,et al.  The Release of Visual Behavior in Toads: Stages of Parallel/Hierarchical Information Processing , 1989 .

[27]  Ronald C. Arkin,et al.  Cooperation without communication: Multiagent schema-based robot navigation , 1992, J. Field Robotics.

[28]  Ronald C. Arkin,et al.  Integrating behavioral, perceptual, and world knowledge in reactive navigation , 1990, Robotics Auton. Syst..

[29]  Christopher G. Langton,et al.  Artificial Life , 2019, Philosophical Posthumanism.

[30]  Alfredo Weitzenfeld,et al.  NSL: neural simulation language , 1998 .

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

[32]  J. Ewert,et al.  Neuroethology , 1980, Springer Berlin Heidelberg.

[33]  Ronald C. Arkin,et al.  Multiagent Mission Specification and Execution , 1997, Auton. Robots.

[34]  Ronald C. Arkin,et al.  Temporal coordination of perceptual algorithms for mobile robot navigation , 1994, IEEE Trans. Robotics Autom..

[35]  Ronald C. Arkin,et al.  Neuroscience in Motion: The Application of Schema Theory to Mobile Robotics , 1989 .

[36]  M. A. Arbib,et al.  Stability and parameter dependency analysis of a facilitation tectal column (FTC) model , 1990, Journal of mathematical biology.

[37]  Stuart C. Shapiro,et al.  Encyclopedia of artificial intelligence, vols. 1 and 2 (2nd ed.) , 1992 .

[38]  D. McFarland,et al.  Intelligent behavior in animals and robots , 1993 .

[39]  R Lara,et al.  A global model of the neural mechanisms responsible for visuomotor coordination in toads. , 1984, Journal of theoretical biology.

[40]  Jonathan H. Connell,et al.  A colony architecture for an artificial creature , 1989 .

[41]  Michael A. Arbib,et al.  Neural Mechanisms Underlying Direction-Selective Avoidance Behavior , 1993, Adapt. Behav..

[42]  M. Arbib,et al.  Prey-catching and predator avoidance 2: modeling the medullary hemifield deficit , 1991 .

[43]  R. Arkin,et al.  Multiagent Mission Speciication and Execution , 1997 .

[44]  Héctor Maldonado,et al.  Ontogeny of the behaviour in the praying mantis , 1973 .

[45]  Peter Ford Dominey,et al.  A cortico-subcortical model for generation of spatially accurate sequential saccades. , 1992, Cerebral cortex.

[46]  O. Grüsser,et al.  Neurophysiology of the Anuran Visual System , 1976 .

[47]  Irwin King,et al.  A neural network based testbed for modelling sensorimotor integration in robotic applications , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[48]  Ronald C. Arkin,et al.  Modeling neural function at the schema level: implications and results for robotic control , 1993 .

[49]  F. Cervantes-Pérez,et al.  Modulatory effects on Prey-Recognition in Amphibia: A Theoretical-Experimental study , 1993 .

[50]  J. Ewert,et al.  Visuomotor Coordination: Amphibians, Comparisons, Models, and Robots , 1989 .

[51]  Ronald C. Arkin,et al.  The impact of cybernetics on the design of a mobile robot system: a case study , 1990, IEEE Trans. Syst. Man Cybern..

[52]  Ronald C. Arkin,et al.  Motor Schema — Based Mobile Robot Navigation , 1989, Int. J. Robotics Res..

[53]  Douglas Christopher MacKenzie,et al.  A design methodology for the configuration of behavior-based mobile robots , 1996 .

[54]  A. Weitzenfeld.  ASL : Hierarchy , Composition , Heterogeneity , and Multi-Granularity in Concurrent Object-Oriented Programming , 1993 .

[55]  Luc Steels,et al.  A case study in the behavior-oriented design of autonomous agents , 1994 .

[56]  Michael A. Arbib,et al.  A concurrent object-oriented framework for the simulation of neural networks , 1991, OOPS Messenger.

[57]  Dave Cliff,et al.  Creatures: artificial life autonomous software agents for home entertainment , 1997, AGENTS '97.