An integrated architecture for motion-control and path-planning

We consider the problem of learning how to control a plant with non-linear control characteristics and solving the path-planning problem at the same time. The solution is based on a path-planning model that designates a speed eld to be tracked, the speed eld being the gradient of the equilibrium solution of a di usion-like process which is simulated on an arti cial neural network by spreading activation. The relaxed di usion eld serves as the input to the interneurons which detect the strength of activity ow in between neighboring discretizing neurons. These neurons then emit the control signals to control neurons which are linear elements. The interneuron to control-neuron connections are trained by a variant of Hebb's rule during control. The proposed method, whose most attractive feature is that it integrates reactive path-planning and continuous motion control in a natural fashion, can be used for learning redundant control problems. 2

[1]  Klaus Schulten,et al.  Topology-conserving maps for learning visuo-motor-coordination , 1989, Neural Networks.

[2]  Shaul Markovitch,et al.  Learning Novel Domains Through Curiosity and Conjecture , 1989, IJCAI.

[3]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..

[4]  Lionel Tarassenko,et al.  Robot path planning using VLSI resistive grids , 1993 .

[5]  Narendra Ahuja,et al.  Gross motion planning—a survey , 1992, CSUR.

[6]  Thomas Martinetz,et al.  Topology representing networks , 1994, Neural Networks.

[7]  P. van der Smagt,et al.  The locally linear nested network for robot manipulation , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[8]  Bartlett W. Mel MURPHY: A Robot that Learns by Doing , 1987, NIPS.

[9]  Michael I. Jordan Motor Learning and the Degrees of Freedom Problem , 2018, Attention and Performance XIII.

[10]  András Lörincz,et al.  Approximate geometry representations and sensory fusion , 1996, Neurocomputing.

[11]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[12]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[13]  Eduardo D. Sontag,et al.  Some Topics in Neural Networks and Control , 1993 .

[14]  Mitsuo Kawato,et al.  Feedback-error-learning neural network for trajectory control of a robotic manipulator , 1988, Neural Networks.

[15]  Pierre Priouret,et al.  Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.

[16]  Roderic A. Grupen,et al.  The applications of harmonic functions to robotics , 1993, J. Field Robotics.

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

[18]  S. Shankar Sastry,et al.  Adaptive Control of Mechanical Manipulators , 1987, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[19]  Bernd Fritzke,et al.  Let It Grow - Self-Organizing Feature Maps With Problem Dependent Cell Structure , 1991 .

[20]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[21]  András Lörincz,et al.  Topology Learning Solved by Extended Objects: A Neural Network Model , 1993, Neural Comput..

[22]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[23]  Stephen Grossberg,et al.  Neural dynamics of adaptive sensory-motor control : ballistic eye movements , 1986 .

[24]  F. Downton Stochastic Approximation , 1969, Nature.

[25]  A. Sideris,et al.  A multilayered neural network controller , 1988, IEEE Control Systems Magazine.

[26]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[27]  Frank L. Lewis,et al.  Neural net robot controller with guaranteed tracking performance , 1995, IEEE Trans. Neural Networks.

[28]  W. Thomas Miller,et al.  Sensor-based control of robotic manipulators using a general learning algorithm , 1987, IEEE J. Robotics Autom..

[29]  T. Fomin,et al.  Self-organizing neurocontrol , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[30]  Stan C. A. M. Gielen,et al.  Neural Network Dynamics for Path Planning and Obstacle Avoidance , 1995, Neural Networks.