Anthropomorphic hands that mimic the smoothness of human hand motions should be controlled by artificial units of high biological plausibility. Adaptability is among the characteristics of such control units, which provides the anthropomorphic hand with the ability to learn motions. This paper presents a simple structure of an adaptive spiking neural network implemented in analogue hardware that can be trained using Hebbian learning mechanisms to rotate the metacarpophalangeal joint of a robotic finger towards targeted angle intervals. Being bioinspired, the spiking neural network drives actuators made of shape memory alloy and receives feedback from neuromorphic sensors that convert the joint rotation angle and compression force into the spiking frequency. The adaptive SNN activates independent neural paths that correspond to angle intervals and learns in which of these intervals the rotation the finger rotation is stopped by an external force. Learning occurs when angle-specific neural paths are stimulated concurrently with the supraliminar stimulus that activates all the neurons that inhibit the SNN output stopping the finger. The results showed that after learning, the finger stopped in the angle interval in which the angle-specific neural path was active, without the activation of the supraliminar stimulus. The proposed concept can be used to implement control units for anthropomorphic robots that are able to learn motions unsupervised, based on principles of high biological plausibility.
Recently, neuromorphic sensors, which convert analogue signals to spiking frequencies, have been reported for neurorobotics. In bio-inspired systems these sensors are connected to the main neural unit to perform post-processing of the sensor data. The performance of spiking neural networks has been improved using optical synapses, which offer parallel communications between the distanced neural areas but are sensitive to the intensity variations of the optical signal. For systems with several neuromorphic sensors, which are connected optically to the main unit, the use of optical synapses is not an advantage. To address this, in this paper we propose and experimentally verify optical axons with synapses activated optically using digital signals. The synaptic weights are encoded by the energy of the stimuli, which are then optically transmitted independently. We show that the optical intensity fluctuations and link’s misalignment result in delay in activation of the synapses. For the proposed optical axon, we have demonstrated line of sight transmission over a maximum link length of 190 cm with a delay of 8 μs. Furthermore, we show the axon delay as a function of the illuminance using a fitted model for which the root mean square error (RMS) similarity is 0.95.
This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.
Due to the high sensitivity to internal parameters, the operation of the chaotic systems may couple strongly to the ambient and are apt to detect small variations of external factors that influence the internal ones. We present details of a method and related equipment for analyzing and characterizing the behavior of chaotic systems, suited for assessing changes in the dynamics. The method essentially consists in determining the probability that the trajectory of the chaotic system is located in a specified region of the phase space. The method is based on the measurement of the time intervals spent by the trajectory in the specified phase space interval. The measuring equipment is a high precision time measuring one, based on microcontrollers. We exemplify the method by assessing the dependency of the behavior of a chaotic electronic circuit on the variation of the power supply voltage.
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