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Many animals can navigate by means of Path Integration (PI), in which an animal keeps a continuously updated record of its current direction and distance from some reference point as it moves away from that place. Equipped with such knowledge, a navigational network learned how to maintain a vector pointing to the home base, with a precision that depends on the number of neurons used to encode the...
In this paper, a set of radial basis function (RBF) neural networks, capable to learn the kinematic and dynamic behavior of the Romeo 4R autonomous vehicle, is presented. In order to obtain a set of good RBF nets in terms of the number of neurons and the number of lagged inputs, a multi-objective genetic algorithm (MOGA) has been used. The kinematic and dynamic systems of the mobile robot have been...
In this study, we investigate the utilization of a multi-objective approach in evolving artificial neural networks (ANNs) for an autonomous mobile robot. The ANN acts as a controller for radio frequency (RF)-localization behavior of a Khepera robot simulated in a 3D physics-based environment. The elitist Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal...
This paper examines the feasibility of evolving analog neuromorphic devices to control flight in a realistic flapping-wing mechanical insect model. It will summarize relevant prior results in controlling a legged robot and explain why these results are relevant to the problem of winged flight. Following, it will present the outcomes of experiments to evolve flight controllers and discuss the implications...
This paper focuses on the application of reinforcement learning to obstacle avoidance in dynamic environments. Behavior-based control architecture is more robust and better in real-time performance than conventional model based architecture in the control of mobile robot. An intelligent controller is proposed by integrating reinforcement learning with the behavior-based control architecture and applied...
Mobile robots need autonomy to fulfill their tasks. Such autonomy is related with their capacity to explorer and to recognize their navigation environments. In this context, the present work considers techniques for the classification and extraction of features from images, using artificial neural networks. This images are used in the mapping and localization system of LACE (automation and evolutive...
This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation of mobile robots, with emphasis on neuro-endocrine interactions. The AHS is composed of two modules, each one associated with a particular reactive task and both implemented using an extended version of the GasNet neural model, denoted spatially unconstrained GasNet model or simply non-spatial GasNet (NSGasNet)...
We here introduce a novel adaptive controller for autonomous mobile robot that binds N types of sensory information. For each sensory modality, sensory-motor connection is made by a three-layered spiking neural network (SNN). The synaptic weights in the model have the property of spike timing-dependent plasticity (STDP) and regulated by presynaptic modulation signal from the sensory neurons. Each...
The paper discussed a new control architecture for bio-mimetic robot, hybrid control architecture combines the best aspects of reactive and deliberative control and is proven to be the most suitable controller for autonomous bio-mimetic robots. The hybrid control architecture consist a command/coordinating level with computed discrete time map-based (DTM) neuronal networks and the central pattern...
The paper presents an original approach for visual identification of road direction of an autonomous vehicle using a neural network classifier called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of neural modules. We present the experimental results obtained by computer simulation of our model. The path to be identified has been quantized in 5 output directions...
In this paper we exercise the genetic programming of a artificial neural network (ANN) that integrates sensor vision, path planning and steering control of a mobile robot. The training of the ANN is done by a simulation of the robot, its sensors, and environment. The results of each simulation run are then used to denote the ability for the tested network to operate the robot. After less than hundred...
In this paper we consider the problem of non-metric/qualitative environment representation for frescoes based autonomous robot navigation. Within this framework, the problem of selecting meaningful set of frescoes and some possible solutions are analysed and compared. Experimental results using six different approaches (resemblance, barycentre, Hamming and Levenshtein distances, cross-correlation...
Real-time collision-free path planning and tracking control of a nonholonomic mobile robot in a dynamic environment is investigated using a neural dynamics based approach. The real-time robot path is generated through a dynamic neural activity landscape of a topologically organized neural network that represents the changing environment. The dynamics of each neuron is characterized by an additive...
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