Current Issue : October - December Volume : 2012 Issue Number : 4 Articles : 6 Articles
The goal of this overview paper is to serve as a reference for researchers that are interested in the realistic modeling of wireless\r\nchannels for the purpose of analysis and optimization of networked robotic systems. By utilizing the knowledge available in\r\nthe wireless communication literature, we first summarize a probabilistic framework for the characterization of the underlying\r\nmultiscale dynamics of a wireless link. We furthermore confirm this framework with our robotic testbed, by making an extensive\r\nnumber of channel measurements. To show the usefulness of this framework for networked robotic applications, we then\r\ndiscuss a few recent examples where this probabilistic channel characterization has been utilized for the theoretical analysis\r\nand communication-aware design of networked robotic systems. Finally, we show how to develop a realistic yet simple channel\r\nsimulator, which can be used to verify cooperative robotic operations in the presence of realistic communication links....
We here introduce a novel biologically inspired adaptive controller for autonomous robot. The proposed controller binds N number of Aplysia-like spiking neural network each of which could interact with a particular sensory information and produce various motors output. The post-synaptic weights in each model are gradually updated by the property of spike timing-dependent plasticity (STDP) and that of the presynaptic modulation signal (synapse-on-synapse contact) from the sensory neurons. Information from different types of sensors is bound at the motor neurons. Experimental results show that a physical robot Khepera with the proposed controller quickly adapted into an open environment by evolving obstacle avoidance behavior while locating a target object using both its IR sensors and liner-camera. We believe that this novel approach could be an opportunity for new applications to autonomous robots with various sensory and motor modalities....
Whether sensor model�s inaccuracies are a result of poor initial modeling or from sensor damage or drift, the effects can be just\r\nas detrimental. Sensor modeling errors result in poor state estimation. This, in turn, can cause a control system relying upon\r\nthe sensor�s measurements to become unstable, such as in robotics where the control system is applied to allow autonomous\r\nnavigation. A technique referred to as a neural extended Kalman filter (NEKF) is developed to provide both state estimation in a\r\ncontrol loop and to learn the difference between the true sensor dynamics and the sensor model. The technique requires multiple\r\nsensors on the control system so that the properly operating and modeled sensors can be used as truth. The NEKF trains a neural\r\nnetwork on-line using the same residuals as the state estimation. The resulting sensor model can then be reincorporated fully into\r\nthe system to provide the added estimation capability and redundancy....
Online path planning (OPP) for unmanned aerial vehicles (UAVs) is a basic issue of intelligent flight and is indeed a dynamic multi-objective optimization problem (DMOP). In this paper, an OPP framework is proposed in the sense of model predictive control (MPC) to continuously update the environmental information for the planner. For solving the DMOP involved in the MPC we propose a dynamic multi-objective evolutionary algorithm based on linkage and prediction (LP-DMOEA). Within this algorithm, the historical Pareto sets are collected and analysed to enhance the performance. For intelligently selecting the best path from the output of the OPP, the Bayesian network and fuzzy logic are used to quantify the bias to each optimization objective. The DMOEA is validated on three benchmark problems characterized by different changing types in decision and objective spaces. Moreover, the simulation results show that the LP-DMOEA overcomes the restart method for OPP. The decision-making method for solution selection can assess the situation in an adversarial environment and accordingly adapt the path planner....
In this paper we introduce a task-based method for designing underactuated multi-joint prosthetic hands for specific grasping tasks. The designed robotic hands or prosthetic hands contain fewer independent actuators than joints. We chose a few specific grasping tasks that are frequently repeated in everyday life and analysed joint motions of the hand during the completion of each task and the level of participation of each joint. The information was used for the synthesis of dedicated underactuated mechanisms that can operate in a low dimensional task coordinate space. We propose two methods for reducing the actuators� number. The kinematic parameters of the synthesized mechanism are determined by using a numerical approach. In this study the joint angles of the synthesized hand are considered as linearly dependent on the displacements of the actuators. We introduced a special error index that allowed us to compare the original trajectory and the trajectory performed by the synthesized mechanism, and to select the kinematic parameters of the new kinematic structure as a way to reduce the error. The approach allows the design of simple gripper mechanisms with good accuracy for the preliminary defined tasks....
In order to explore issues of humanoid robotic facial expression, this paper focuses on the study and design approach of the anthropomorphic head. In all, robotic head has a total of 12 mechanical degree of freedom which mimic the features of human head. This study discusses the development of an intelligent robot, looking into expressive social exchange between humans and humanoid robot. Robot which has emotion decision making capabilities and behavioral basic facial expression such as happy, anger, disgust, sad, surprise, calm, interest. These seven basic facial expressions of the robot are realized using multi-servomotor co-operative control....
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