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This paper explores the use of a mechanism to auto-regulate the robot behavior in situations of persistent failures. In order to give more autonomy to a mobile robot, a generical frustration mechanism based on the automonitoring of the progress (in terms of goal distance reduction) is studied in different situations and on different parts of the robot architecture. To escape failure situations and...
We have created a brain-machine hybrid system (BMHS) which is able to solve the chemical plume tracking (CPT) problem using the brain of the male silkworm moth. The purpose of the system is to investigate adaptability which results from interactions between brain, body, and environment. In this paper, we describe a BMHS architecture and experiments to verify that the behavior of the BMHS is similar...
Gait pattern planning is an important issue in robotic gait rehabilitation. Gait pattern is known to be related to gait parameters, such as cadence, stride length, and walking speed. Thus, prior before the discussion of gait pattern planning, the planning of gait parameters for natural walking should be addressed. This work utilizes multi-layer perceptron neural network (MLPNN) to predict natural...
We propose a pure topological recurrent networks controller, which has random binary connections in hidden layer, and all hidden neurons are activated by sinusoidal functions. A direct graph encoding method and four genetic operators are implemented for using genetic programming to train this controller. Firstly, its feasibility and efficiency were validated by a pair of function approximation experiments,...
We have studied and developed the behavior of two specific neural processes, used for vehicle driving and path planning, in order to control mobile robots. Each processor is an independent agent defined by a neural network trained for a defined task. Through simulated evolution fully trained agents are encouraged to socialize by opening low bandwidth, asynchronous channels between them. Under evolutive...
In this paper, a novel framework is proposed to incorporate task assignment, path planning, and tracking control of a multi-robot system. The dynamic task assignment of multi-robots is achieved using a self-organizing map based feature. The real-time collision-free robot path is generated from a neuro-dynamics network through sensor measurement and responding immediately to dynamic elements in the...
In order to resolve the computational complexity for local map matching of hierarchical simultaneous localization and mapping (SLAM), a novel self-organizing fuzzy neural networks (SOFNN) based approach was proposed in this paper. The matching component for local maps in the hierarchical SLAM is realized by an SOFNN. A subset of signature elements included in a local map was chosen by a clustering...
In this paper, we investigate the utilization of a multi-objective approach for evolving artificial neural networks (ANNs) that act as controllers for a radio frequency (RF) based collective box-pushing task of a group of virtual E-puck robots simulated in a 3D, physics-based environment. The modified Pareto-frontier Differential Evolution (PDE) algorithm is used to generate the Pareto optimal sets...
Autonomous robotic odour source classification and localization in real world environments is an essential step for applications such as humanitarian demining, environmental monitoring or search and rescue operations. However, at the moment this problem has only been solved by nature (e.g.: moths, bees, rats, dogs). Biological systems are capable and efficient at odour source localization in spite...
In order to solve the problem of dimension disaster, which may be produced by applying Q-learning to intelligent system of continuous state-space, we proposed a Q-learning algorithm based on ART2 in this paper, and give the specific steps. Through introducing the ART2 neural network in the Q-learning algorithm, Q-learning Agent in view of the duty which needs to complete to learn an appropriate incremental...
In this paper, a Novel Cellular Neural Network (CNN) entitled the shortest path CNN (SP-CNN) is proposed. It has a good performance in path planning for mobile robots because of its network structure and neural dynamics. The proposed method not only can generate the best solution in static environments in real time but also generate the optional solution in dynamic environments or in unknown environments...
Executive control incorporates cognitive functions involved in the control and management of other cognitive processes. Such high-level skills are hard to be explored with brain imaging studies because they require complex and persistent experimental procedures. Alternatively, computational modeling may provide a new way to indirectly explore executive control mechanisms. The current work adopts this...
This paper describes the development of a FPGA-based object detection algorithm for manipulation purposes in a mobile robot. The target application is a robotic system which aids workers in a manufacturing plant. The whole system is provided with a camera which captures images of the objects that can be found in the environment. The FPGA extracts the most useful data from these images and performs...
A number of studies have demonstrated the capability of ANNs for the required robot behaviors by using an evolutionary optimization technique in generating more complex robot controllers. Interestingly however, there is still a serious lack of research in exploring the application of Evolutionary Multi-objective Optimization (EMO) algorithm in evolutionary robotics. In this paper, we investigate the...
In this paper, a Spiking Neural Network (SNN) based controller is designed to fulfill the task of formation control of multiple mobile robots. The neural network contains three layers with different neuron model for different layer: the input layer encodes the inputs including sensor and task-related information by leaky integrate-and-fire (LIF) neurons, the hidden layer uses the approximate coincidence...
This paper describes the path planning method based on biologically inspired neural network and proposes a novel robot control system. Firstly, the principle of the biologically inspired neural network is introduced and its application is analyzed. Secondly, the computer modeling and simulation are conducted and show that this method is effective. Moreover, path planning experiments are accomplished...
Robot chair control using an asynchronous brain machine interface (ABMI) based on motor imagery requires sufficient subject training. This paper proposes a generalized a brain machine interface design to investigate the feasibility of real-time robot chair control by trained subjects. Performance of the real-time experiments conducted for asynchronous navigation is assessed based on completion of...
A novel approach of reducing computation complexity for simultaneous localization and mapping (SLAM) of mobile robots, which based on self-organizing fuzzy neural networks (SONN) was proposed in this paper. The matching component for local maps in the atlas SLAM system is replaced by an SONN. Our approach is superior to the original one by taking all of the feature parameters in a local map instead...
This paper introduces the principle of biologically inspired neural network and analyzes the deficiency of this algorithm. Then, the road edge recognition algorithm and both static and moving obstacles detection method are described. On the basis of comparing the differences between indoor full coverage cleaning and outdoor, some improved full coverage path planning methods based on biologically inspired...
Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community. In order to execute a safe navigation on outdoors it is necessary to identify parts of the terrain that can be traversed by the robot and parts that should be avoided. This paper describes an analyses of an image-based terrain identification based on different visual information using a multi-layer...
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