Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks

Hulea, Mircea; Burlacu, Adrian; Caruntu, Constantin F.

doi:10.1142/s0219843619500129

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…Electronic synapses model presynaptic elements of learning such as PTP and the postsynaptic plasticity that determines Hebbian learning via long-term potentiation (LTP). In addition, a synapse stores the synaptic weight using a capacitor that can be charged or discharged in real time using cheap circuits [ 26 , 27 ]. Electronic synapses generate excitatory or inhibitory spikes to feed the corresponding

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Section: Methodsmentioning

confidence: 99%

The Influence of the Number of Spiking Neurons on Synaptic Plasticity

2023

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The main advantages of spiking neural networks are the high biological plausibility and their fast response due to spiking behaviour. The response time decreases significantly in the hardware implementation of SNN because the neurons operate in parallel. Compared with the traditional computational neural network, the SNN use a lower number of neurons, which also reduces their cost. Another critical characteristic of SNN is their ability to learn by event association that is determined mainly by postsynaptic mechanisms such as long-term potentiation. However, in some conditions, presynaptic plasticity determined by post-tetanic potentiation occurs due to the fast activation of presynaptic neurons. This violates the Hebbian learning rules that are specific to postsynaptic plasticity. Hebbian learning improves the SNN ability to discriminate the neural paths trained by the temporal association of events, which is the key element of learning in the brain. This paper quantifies the efficiency of Hebbian learning as the ratio between the LTP and PTP effects on the synaptic weights. On the basis of this new idea, this work evaluates for the first time the influence of the number of neurons on the PTP/LTP ratio and consequently on the Hebbian learning efficiency. The evaluation was performed by simulating a neuron model that was successfully tested in control applications. The results show that the firing rate of postsynaptic neurons post depends on the number of presynaptic neurons pre, which increases the effect of LTP on the synaptic potentiation. When post activates at a requested rate, the learning efficiency varies in the opposite direction with the number of pres, reaching its maximum when fewer than two pres are used. In addition, Hebbian learning is more efficient at lower presynaptic firing rates that are divisors of the target frequency of post. This study concluded that, when the electronic neurons additionally model presynaptic plasticity to LTP, the efficiency of Hebbian learning is higher when fewer neurons are used. This result strengthens the observations of our previous research where the SNN with a reduced number of neurons could successfully learn to control the motion of robotic fingers.

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Section: Methodsmentioning

confidence: 99%

The Influence of the Number of Spiking Neurons on Synaptic Plasticity

2023

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“…Also, using few neurons, the SNN learned to activate the inhibitory neurons according to the angle interval where the finger was stopped by the external force. In this work, we did not focus on the precision and accuracy of the finger positioning because these parameters were analysed previously using a similar system based on SNN and SMA actuators [ 17 ]. Also, the SNN was not able to stop the finger exactly at the same angle at which it was blocked by the obstacle because the angle intervals considered were wide.…”

Section: Experimental Investigationmentioning

confidence: 99%

“…Thus, in this work, the artificial muscles are implemented with shape memory alloy (SMA) wires that actuate by contraction, as do biological muscles [ 12 , 13 , 14 ], and their contraction strength can be determined directly by the frequency of the electronic spiking neurons [ 15 , 16 ]. The results reported previously [ 17 ] show that, despite the slowness and nonlinearity of SMA wires [ 18 ], a small SNN with a bioinspired structure [ 19 ] is able to control the rotation angle of a SMA-actuated robotic joint when the arm moves towards target positions. In that case, the spiking neural network behaves as a regulator for the rotation angle, even when the arm is slightly loaded.…”

Section: Introductionmentioning

confidence: 99%

Adaptive SNN for Anthropomorphic Finger Control

Hulea

Uleru

Caruntu

2021

Sensors

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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.

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“…Recently, superior abilities of the brain such as symmetry perception [ 7 ], visual pattern recognition [ 8 ], and speech recognition [ 9 , 10 ] were modelled using neuromorphic hardware based on spiking neurons. In robotics, different types of neural systems were designed for the control of vehicles speed [ 11 ] or trajectory [ 12 ], as well as for modelling the motion abilities of the human body including the control of robotic arms [ 13 , 14 , 15 ] or anthropomorphic fingers [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Control System for Fingers Actuated by Multiple SMA Actuators

2022

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Spiking neural networks are able to control with high precision the rotation and force of single-joint robotic arms when shape memory alloy wires are used for actuation. Bio-inspired robotic arms such as anthropomorphic fingers include more junctions that are actuated simultaneously. Starting from the hypothesis that the motor cortex groups the control of multiple muscles into neural synergies, this work presents for the first time an SNN structure that is able to control a series of finger motions by activation of groups of neurons that drive the corresponding actuators in sequence. The initial motion starts when a command signal is received, while the subsequent ones are initiated based on the sensors’ output. In order to increase the biological plausibility of the control system, the finger is flexed and extended by four SMA wires connected to the phalanges as the main tendons. The results show that the artificial finger that is controlled by the SNN is able to smoothly perform several motions of the human index finger while the command signal is active. To evaluate the advantages of using SNN, we compared the finger behaviours when the SMA actuators are driven by SNN, and by a microcontroller, respectively. In addition, we designed an electronic circuit that models the sensor’s output in concordance with the SNN output.

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Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks

Cited by 7 publications

References 17 publications

The Influence of the Number of Spiking Neurons on Synaptic Plasticity

The Influence of the Number of Spiking Neurons on Synaptic Plasticity

Adaptive SNN for Anthropomorphic Finger Control

Bio-Inspired Control System for Fingers Actuated by Multiple SMA Actuators

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