A Spike-Latency Model for Sonar-Based Navigation in Obstacle Fields

The rapid control of sonar-guided vehicles through obstacle fields has been a goal of robotics for decades. How sensory data are represented strongly affects how obstacles and goal information can be combined to select a direction of travel. Many approaches combine attractive and repulsive effects to steer; we have implemented an algorithm that first evaluates the desirability of different directions followed by a winner-take-all (WTA) mechanism to guide steering. We describe a neuromorphic VLSI implementation of this algorithm using the inherent echo delay of obstacles to produce a range-dependent gain in a ldquorace-to-first-spikerdquo neural WTA circuit.

[1]  Robert M. McPeek,et al.  Deficits in saccade target selection after inactivation of superior colliculus , 2004, Nature Neuroscience.

[2]  Brett R. Fajen,et al.  Visual navigation and obstacle avoidance using a steering potential function , 2006, Robotics Auton. Syst..

[3]  Philipp Häfliger,et al.  A time domain winner-take-all network of integrate-and-fire neurons , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[4]  John G. Harris,et al.  Time-based arithmetic using step functions , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[5]  Yoram Koren,et al.  Histogramic in-motion mapping for mobile robot obstacle avoidance , 1991, IEEE Trans. Robotics Autom..

[6]  Kwabena Boahen Retinomorphic Vision Systems 11: Communication Channel Design , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.

[7]  Christopher J. Bishop,et al.  Pulsed Neural Networks , 1998 .

[8]  Sidney S. Simon,et al.  Merging of the Senses , 2008, Front. Neurosci..

[9]  Tobi Delbrück,et al.  A Multichip Pulse-Based Neuromorphic Infrastructure and Its Application to a Model of Orientation Selectivity , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[10]  Timothy K. Horiuchi,et al.  A Neuromorphic VLSI Model of Bat Interaural Level Difference Processing for Azimuthal Echolocation , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  Sebastian Scherer,et al.  Learning obstacle avoidance parameters from operator behavior , 2006, J. Field Robotics.

[12]  Kwabena Boahen,et al.  A 48,000 pixel, 590,000 transistor silicon retina in current-mode subthreshold CMOS , 1994, Proceedings of 1994 37th Midwest Symposium on Circuits and Systems.

[13]  Timothy K. Horiuchi A neural model for sonar-based navigation in obstacle fields , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[14]  H. Sompolinsky,et al.  13 Modeling Feature Selectivity in Local Cortical Circuits , 2022 .

[15]  Giacomo Indiveri,et al.  A VLSI reconfigurable network of integrate-and-fire neurons with spike-based learning synapses , 2004, ESANN.

[16]  T. Delbruck 'Bump' circuits for computing similarity and dissimilarity of analog voltages , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[17]  G. Schöner,et al.  Dynamic Field Theory of Movement Preparation , 2022 .

[18]  Richard Hans Robert Hahnloser,et al.  Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit , 2000, Nature.

[19]  E. Culurciello,et al.  A biomorphic digital image sensor , 2003, IEEE J. Solid State Circuits.