Biological and cognitive foundations of intelligent sensor fusion

This paper reviews the literature from the biological and cognitive sciences in sensory integration and derives principles for use in constructing intelligent sensor fusion systems. In particular, it presents psychophysical and neurophysical studies on how sensor fusion is accomplished and cognitive models of associated activities, including optimization of sensing configurations, improvement of sensing quality, and filtering of noise. The sensor fusion effects architecture for robot navigation is also presented as one example of how these insights from the biological and computer science can be applied to robotic sensor fusion. Experimental results demonstrates the utility of the biological and cognitive insights, especially that of fusion modes. Other representative architectures for robotic sensor fusion are contrasted with the biological and cognitive principles.

[1]  Robert P. Erickson,et al.  Common Properties of Sensory Systems , 1978 .

[2]  Terrance L. Huntsberger,et al.  Comparison Of Techniques For Sensor Fusion Under Uncertain Conditions , 1989, Optics East.

[3]  Ronald C. Arkin,et al.  Motor schema based navigation for a mobile robot: An approach to programming by behavior , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

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

[5]  Hans P. Moravec.  Sensor Fusion in Certainty Grids for Mobile Robots , 1988, AI Mag..

[6]  Dennis M. Buede,et al.  Data Fusion and Decision Support for Command and Control , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Hugh Durrant-Whyte,et al.  Integration, coordination, and control of multi-sensor robot systems , 1987 .

[8]  L. Marks.  The Unity of the Senses: Interrelations Among the Modalities , 1978 .

[9]  Pramod K. Varshney,et al.  A Distributed Artificial Intelligence Approach To Object Identification And Classification , 1989, Defense, Security, and Sensing.

[10]  G. T. Chavez,et al.  Exception handling for sensor fusion , 1993, Other Conferences.

[11]  R. Welch.  Perceptual modification : adapting to altered sensory environments / Robert B. Welch , 1978 .

[13]  G. E. Alexander,et al.  The Basal Ganglia and Sensorimotor Integration , 1987 .

[14]  G. Schneider.  Two visual systems: Brain mechanisms for localization and discrimination are dissociated by tectal a , 1969 .

[15]  John P. Zubek,et al.  Sensory Deprivation: Fifteen years of research , 1969 .

[16]  Robin R. Murphy,et al.  Autonomous navigation in a manufacturing environment , 1990, IEEE Trans. Robotics Autom..

[17]  H. E. Stephanou,et al.  Perspectives on imperfect information processing , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[18]  Edward M. Riseman,et al.  Towards cosmopolitan robots: intelligent navigation in extended man-made environments , 1987 .

[19]  B. Abernethy,et al.  Chapter 1 The Rise and Fall of Dominant Paradigms in Motor Behaviour Research , 1992 .

[20]  Michael Drumheller,et al.  Mobile Robot Localization Using Sonar , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[22]  G. Reynolds,et al.  A Method for Initial Hypothesis Formation in Image Understanding , 1987 .

[23]  A. Struppler,et al.  Clinical Aspects of Sensory Motor Integration , 1987, Advances in Applied Neurological Sciences.

[24]  J. Lackner.  Some Aspects of Sensory-Motor Control and Adaptation in Man , 1981 .

[25]  Steven A. Shafer,et al.  An architecture for sensor fusion in a mobile robot , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[26]  Ren C. Luo,et al.  Multisensor integration and fusion in intelligent systems , 1989, IEEE Trans. Syst. Man Cybern..

[27]  Robin R. Murphy,et al.  Sfx: An Architecture For Action-oriented Sensor Fusion , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Robin Roberson Murphy.  An architecture for intelligent robotic sensor fusion , 1992 .

[29]  Robin R. Murphy.  Control scheme for sensor fusion for navigation of autonomous mobile robots , 1991, Other Conferences.

[30]  James J. Clark,et al.  Data Fusion for Sensory Information Processing Systems , 1990 .

[31]  G. Schneider.  Two visual systems. , 1969, Science.

[32]  Gregory D. Hager,et al.  Task-Directed Sensor Fusion and Planning , 1990 .

[33]  John D. Lowrance,et al.  An Inference Technique for Integrating Knowledge from Disparate Sources , 1981, IJCAI.

[34]  M. Goodale,et al.  Neural Mechanisms of Visual Orientation in Rodents: Targets Versus Places , 1983 .

[35]  Tracy L. Anderson,et al.  Animal behavior as a paradigm for developing robot autonomy , 1990, Robotics Auton. Syst..

[36]  Louis-François Pau,et al.  Sensor data fusion , 1988, J. Intell. Robotic Syst..

[37]  P. L. Bogler,et al.  Shafer-dempster reasoning with applications to multisensor target identification systems , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[38]  D. B. Judd.  Standard Response Functions for Protanopic and Deuteranopic Vision , 1944 .

[39]  Doug Morgan,et al.  Multisensor Object Recognition From 3D Models , 1989, Optics East.

[40]  I. Block.  Aristotle, The Power of Perception , 1992 .

[41]  James M. Keller,et al.  Uncertainty Management In A Rule-Based Automatic Target Recognizer , 1989, Defense, Security, and Sensing.

[42]  J. Summers.  Approaches to the study of motor control and learning , 1992 .