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The dynamics of complex neural networks modelling self-organized process in cortical maps, like unsupervised competitive neural networks (UCNN), are based on the standard competitive learning law to determine the best-matching representant among all neurons for a given input. However, UCNNs include aspects of long and short-term memory, which are characterized by an equation of neural activity as...
Implementing the neural mechanism of animal cognition in mobile robots control is a natural way to make the development of bio-inspired autonomous robots with more robust functionality. The neuroscience researches confirm that grid cells play a special role in animals' spatial navigation. This paper presents a design scheme that implements the continuous attractor neural network model of grid cells...
The autonomous navigation of mobile robots in unknown environments is of great interest in mobile robotics. This article discusses a new strategy to navigate to a known target location in an unknown environment using a combination of the “go-to-goal” approach and reinforcement learning with biologically realistic spiking neural networks. While the “go-to-goal” approach itself might lead to a solution...
Snake-like robots can adapt to the changes in the dynamic environments. Compared with developing a complex controller, a simple compliant mechanism can inherently improve the performance of the snake-like robot. In this paper, a simulation study on the effects of the compliant intervertebral discs designed for the planar snake-like robot is carried out. And a two-layered central pattern generator...
This paper describes a cognitive map building system based on episodic memory for mobile robots. Inspired by the biological findings on the mammalian hippocampus, we proposed a novel method of episodic memory modeling which including a sequence of experienced observations, neutral states, behaviors and pose perceptions. The presented method can autonomously and incrementally learn the environmental...
In this paper we discuss the importance of the ability to perceive and generate affordances, i.e. opportunities for behaviour execution. More specifically we show how robots evolved for the ability to solve a given problem use the ability to generate affordances for displaying differentiated behaviour and/or to regulate how their behaviour vary over time.
For educational purposes there is a need to teach electrical and computer engineering students the basics of the design of state machines using programmable logic devices, and for students interested in mobile robots to teach them the basics of mobile robots' behaviors. At the same time one of the topics of interest in the mobile robot's community is how to generate their new behaviors, using state...
This paper studies the Lego NXT platform's suitability for evolutionary robotics. It is shown that the low-cost Lego NXT educational set is indeed adequate for simple experiments in evolutionary robotics. This is demonstrated by an experiment, where an artificial neural network-based controller capable of behaving meaningfully in a Lego sumo wrestling context is evolved on physical Lego NXT robots...
This paper illustrates a method to design dynamic neural network controllers to allow a robot to autonomously navigate roads based on color perception. The neuro-controller moves the robot by setting the speed of the wheels and adjusts the robot visual systems by setting the value of three parameters that determine how much of the red, green and blue components of the RGB camera images contribute...
A coverage motion planning (CMP) is a kind of coverage path planning, which requires the robot path to fill every grid of the workspace. It is an essential issue in plenty of robotic applications. Safety aware collision-free coverage motion planning of an autonomous vehicle is one of the major challenges in intelligent vehicle systems. Many studies have been focused on the obstacle avoidance to prevent...
This paper presents a new BP-neural-network-based localization algorithm for a wheeled agricultural mobile robot, which is front-wheel drive and differential steering. Training the BP neural network is the first step of the localization algorithm. During this process, the drive pulse number is regarded as the input; the length of the left or right wheel's trajectory is regarded as the output; and...
This paper reviews a number of recent algorithms for mobile robot path planning, navigation and motion control, which employ fuzzy logic and neuro-fuzzy learning and reasoning. Starting with a discussion of the structure of fuzzy and neuro-fuzzy systems, two fuzzy obstacle avoidance path planning algorithms are presented followed by a 3-level neuro-fuzzy local and global path planning scheme. Then...
Biological creatures perform their motion by using distributed spinal control system. Natural control generates motion instantly based on the feelings from the environment. In line with this concept, an artificial control system is known as Central Pattern Generator (GPG) is an online motion generation system that can be generated instantly like spine based control system. CPG also generates online...
Development of computational models of the brain is relevant not only for deepening our understanding of the biological system but also for potential applications to various engineering problems. In this paper the implementation of a bi-hemispherical neuronal network model of the cerebellum (biCNN) in a stand-alone, portable real time (RT) device is presented. The biCNN is tested during a control...
On rough terrain, there are a variety of soil types having different soil strength. It means that it is needed to change wheel control strategies since optimal wheel slip levels differ depending on soil strength. Therefore, this paper proposes a novel algorithm for optimal slip control of wheeled robots with a trade-off between traction and energy consumption on the basis of observing a change of...
As the third generation of artificial neural networks (ANNs), spiking neural networks (SNNs) have many advantages over the traditional ones. Selecting proper spiking neuron models for the design of SNNs is important. In this paper, schematic and training algorithm of spiking integrated and fired (IAF) neuron model and probability spiking neuron model (pSNM) are introduced. By comparing the classification...
Living organisms are capable of autonomously adapting to dynamically changing environments by receiving inputs from highly specialized sensory organs and elaborating them on the same parallel, power-efficient neural substrate. In this paper we present a prototype for a comprehensive integrated platform that allows replicating principles of neural information processing in real-time. Our system consists...
According to the motion balance for a two-wheeled robot control problems, we put forward a bionic learning algorithm based on growing cell structure (GCS) network and Q-learning. GCS network has in addition to the competitive mechanism of SOM network, and it can also carry out self-organizationally evolution through the continuous growth of new neurons. Q-learning algorithm is a model free reinforcement...
This paper presents a probabilistic graphical model in the form of a factor graph to perform hierarchical probabilistic inference by computing kinematics of an omnidirectional mobile robot. We propose applying population coding principles to encode messages transmitted within the factor graph to update the network's internal belief, as inspired by neuronal information processing. We examine two inference...
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