Engineering robust intelligent robots

The purpose of this paper is to discuss the challenge of engineering robust intelligent robots. Robust intelligent robots may be considered as ones that not only work in one environment but rather in all types of situations and conditions. Our past work has described sensors for intelligent robots that permit adaptation to changes in the environment. We have also described the combination of these sensors with a "creative controller" that permits adaptive critic, neural network learning, and a dynamic database that permits task selection and criteria adjustment. However, the emphasis of this paper is on engineering solutions which are designed for robust operations and worst case situations such as day night cameras or rain and snow solutions. This ideal model may be compared to various approaches that have been implemented on "production vehicles and equipment" using Ethernet, CAN Bus and JAUS architectures and to modern, embedded, mobile computing architectures. Many prototype intelligent robots have been developed and demonstrated in terms of scientific feasibility but few have reached the stage of a robust engineering solution. Continual innovation and improvement are still required. The significance of this comparison is that it provides some insights that may be useful in designing future robots for various manufacturing, medical, and defense applications where robust and reliable performance is essential.

[1]  F. Lewis,et al.  Neural Network Control of Robot Arms and Nonlinear Systems , 1997 .

[2]  P. J. Werbos,et al.  Backpropagation and neurocontrol: a review and prospectus , 1989, International 1989 Joint Conference on Neural Networks.

[3]  Ernest L. Hall,et al.  Beyond adaptive-critic creative learning for intelligent mobile robots , 2001, SPIE Optics East.

[4]  Bernard Widrow,et al.  Punish/Reward: Learning with a Critic in Adaptive Threshold Systems , 1973, IEEE Trans. Syst. Man Cybern..

[5]  Anthony Stentz,et al.  A mission planning system for multiple mobile robots in unknown, unstructured, and changing environments , 1998 .

[6]  P. J. Werbos,et al.  Generalized maze navigation: SRN critics solve what feedforward or Hebbian nets cannot , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[7]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[8]  Frank L. Lewis,et al.  Robot Manipulator Control: Theory and Practice , 2003 .

[9]  Bernard Widrow,et al.  Neurointerfaces , 2002, IEEE Trans. Control. Syst. Technol..

[10]  Keigo Watanabe,et al.  Control of nonholonomic mobile robot by an adaptive actor-critic method with simulated experience based value-functions , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[11]  Ernest L. Hall,et al.  Creative learning for intelligent robots , 2007, SPIE Optics East.

[12]  Max Donath,et al.  American Control Conference , 1993 .

[13]  G.G. Yen,et al.  Dynamic database approach for fault tolerant control using dual heuristic programming , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[14]  Peter Cao.  AUTONOMOUS RUNWAY SOIL SURVEY SYSTEM WITH THE FUSION OF GLOBAL AND LOCAL NAVIGATION MECHANISM , 2004 .

[15]  Frank L. Lewis,et al.  Adaptive critic neural network for feedforward compensation , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[16]  Alhaj Ali,et al.  TECHNOLOGIES FOR AUTONOMOUS NAVIGATION IN UNSTRUCTURED OUTDOOR ENVIRONMENTS , 2003 .

[17]  Ilya V. Kolmanovsky,et al.  Predictive energy management of a power-split hybrid electric vehicle , 2009, 2009 American Control Conference.

[18]  ERNEST L. HALL,et al.  1 CREATIVE CONTROL FOR INTELLIGENT AUTONOMOUS MOBILE ROBOTS , 2003 .

[19]  Ernest L. Hall,et al.  A framework for the natural-language-perception-based creative control of unmanned ground vehicles , 2004, SPIE Defense + Commercial Sensing.

[20]  Ernest L. Hall,et al.  Perception-based control for intelligent systems , 2006 .

[21]  Ernest L. Hall,et al.  Eclectic theory of intelligent robots , 2007, SPIE Optics East.

[22]  Mitsuo Gen,et al.  Intelligent Engineering Systems through Artificial Neural Networks , 2009 .

[23]  Stability , 1973 .

[24]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .