Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments

Neurobiologically-based control system for an adaptively walking hexapod

Purpose - Biological systems such as insects have often been used as a source of inspiration when developing walking robots. Insects' ability to nimbly navigate uneven terrain, and their observed behavioral complexity have been a beacon for engineers who have used behavioral data and hypothesized control systems to develop some remarkably agile robots. The purpose of this paper is to show how it is possible to implement models of relatively recent discoveries of the stick insect's local control system (its thoracic ganglia) for hexapod robot controllers. Design/methodology/approach - Walking control based on a model of the stick insect's thoracic ganglia, and not just observed insect behavior, has now been implemented in a complete hexapod able to walk, perform goal-seeking behavior, and obstacle surmounting behavior, such as searching and elevator reflexes. Descending modulation of leg controllers is also incorporated via a head module that modifies leg controller parameters to accomplish turning in a role similar to the insect's brain and subesophageal ganglion. Findings - While many of these features have been previously demonstrated in robotic subsystems, such as single- and two-legged test platforms, this is the first time that the neurobiological methods of control have been implemented in a complete, autonomous walking hexapod. Originality/value - The methods introduced here have minimal computation complexity and can be implemented on small robots with low-capability microcontrollers. This paper discusses the implementation of the biologically grounded insect control methods and descending modulation of those methods, and demonstrates the performance of the robot for navigating obstacles and performing phototaxis. © Emerald Group Publishing Limited

Neurobiologically-based control system for an adaptively walking hexapod

Purpose - Biological systems such as insects have often been used as a source of inspiration when developing walking robots. Insects' ability to nimbly navigate uneven terrain, and their observed behavioral complexity have been a beacon for engineers who have used behavioral data and hypothesized control systems to develop some remarkably agile robots. The purpose of this paper is to show how it is possible to implement models of relatively recent discoveries of the stick insect's local control system (its thoracic ganglia) for hexapod robot controllers. Design/methodology/approach - Walking control based on a model of the stick insect's thoracic ganglia, and not just observed insect behavior, has now been implemented in a complete hexapod able to walk, perform goal-seeking behavior, and obstacle surmounting behavior, such as searching and elevator reflexes. Descending modulation of leg controllers is also incorporated via a head module that modifies leg controller parameters to accomplish turning in a role similar to the insect's brain and subesophageal ganglion. Findings - While many of these features have been previously demonstrated in robotic subsystems, such as single- and two-legged test platforms, this is the first time that the neurobiological methods of control have been implemented in a complete, autonomous walking hexapod. Originality/value - The methods introduced here have minimal computation complexity and can be implemented on small robots with low-capability microcontrollers. This paper discusses the implementation of the biologically grounded insect control methods and descending modulation of those methods, and demonstrates the performance of the robot for navigating obstacles and performing phototaxis. © Emerald Group Publishing Limited