Robot vision with cellular neural networks: a practical implementation of new algorithms

Abstract

SUMMARYCellular neural networks (CNNs) are well suited for image processing due to the possibility of a parallel computation. In this paper, we present two algorithms for tracking and obstacle avoidance using CNNs. Furthermore, we show the implementation of an autonomous robot guided using only real-time visual feedback; the image processing is performed entirely by a CNN system embedded in a digital signal processor (DSP). We successfully tested the two algorithms on this robot.

A new scheme for power factor correction and active filtering for six-pulse converters loads

“…References [13][14][15][16][17], and references therein. Indeed, such networks are ideally suited for solving optimization problems.…”

“…Many results on the existence and uniqueness of the equilibrium point, global asymptotic (exponential) stability have been established and applied to signal and image processing system, associate memories, pattern classification and so on. In Reference [13], cellular neural networks (CNNs) were used for tracking and obstacle avoidance in an autonomous robot. In References [14][15], the properties of global exponential convergence of discontinuous neural networks and delayed bidirectional associative memory neural networks (DBAMNN) were discussed.…”

“…Hence two groups of ADALINEs are adopted in the d-axis and q-axis of the synchronous rotating frame to derive the dc components in d − q reference frame, corresponding to the fundamental components of load currents in stationary phase a − b − c frame. The drawbacks regarding to the sophisticated network structure and computational complexity in References [13][14][15][16][17] can be significantly simplified, which makes it possible to implement the algorithms in low cost digital signal processor (DSP) and facilitate the practical implementation of the proposed system.…”

“…The proportional-integral (PI) controllers are implemented in daxis and q-axis based on the decoupled state-space equations of the T-type active power filter. The load side dynamics are incorporated in the feed-forward control loop by using two groups of synchronous frame adaptive linear neural networks (SADALINEs) [13][14][15][16][17][18][19][20][21][22][23] in d-axis and q-axis, respectively, for fast estimation of the fundamental components of nonlinear load currents. Besides, a separate proportional-integral (PI) controller is adopted in the outer voltage loop for balancing the active power flow of the voltage source inverter.…”

Single‐layer perceptron and dynamic neuron implementing linearly non‐separable Boolean functions

“…It is believed that the new network structures will find more engineering and technological applications as the works in [20][21][22][23][24] in the near future.…”