In car-like autonomous vehicle systems, it is an essential task of generating the motion commands according to a given path/strategy. Quite a few theories and techniques have been proposed for the generation of the mo...
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ISBN:
(纸本)9781457721977
In car-like autonomous vehicle systems, it is an essential task of generating the motion commands according to a given path/strategy. Quite a few theories and techniques have been proposed for the generation of the motion commands in autonomous vehicles, such as pure pursuit, Stanley's nonlinear feedback, chained-form control of kinematic model and the linearized optimal control of dynamic model. Here a non-linear optimizationalgorithm based on the vehicle's kinematic model and the actuators' model is proposed, which combines the control system dynamic behaviors and gives out the control sequences directly. It starts with modeling the local kinematic behavior and actuators' dynamics. Then online optimizationalgorithm is applied to the objective function of minimizing the energy cost, execution time and tracking error with some trade-off weights among them. The experiments showed that it worked well for vehicles running in maze-like environment.
The detection and localization of improvised explosive devices(IEDs) on the roadside is a new subject encountered in the struggle against terrorism. A novel detection and localization method was proposed for IEDs base...
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The detection and localization of improvised explosive devices(IEDs) on the roadside is a new subject encountered in the struggle against terrorism. A novel detection and localization method was proposed for IEDs based on magnetic signals. Since most of the IEDs have the ferromagnetic properties, the magnetic field produced around the body by the IED can be detected by 3-axis fluxgate sensor array system. With these detected sensor data, the detection and localization of the IED can be computed by an appropriate method based on magnetic dipole model and nonlinear optimization algorithm. This paper studied respectively the properties of explosives and roads as target and environment to be detected. First, the gradient of total magnetic field was directly reconstructed from the magnetic field and the data of sensor array. In order to reduce the effects of the Earth's magnetic field, the total gradient contraction was used to detect the IEDs. Second, the localization and magnetic moment parameters were searched in the rough by adopting the particle swarm optimization (PSO) algorithm and the precision ones were found by using steepest descent method. Simulation results show that the method mentioned achieves good effects, the maximum detection range of the sensor array can reach to 12m for a 15Am(2) target and the mean errors of localization and magnetic moment estimation are less than 0.16m and 1Am(2) respectively. Since the method need not know the IED's magnetic moment in advance, it is adapt to battlefield environment. In addition, this detection method can be directly applied to solve the problem of detecting and localizing underwater IEDs. (C) 2010 Published by Elsevier Ltd.
The detection and localization of improvised explosive devices(IEDs) on the roadside is a new subject encountered in the struggle against terrorism. A novel detection and localization method was proposed for IEDs base...
详细信息
The detection and localization of improvised explosive devices(IEDs) on the roadside is a new subject encountered in the struggle against terrorism. A novel detection and localization method was proposed for IEDs based on magnetic signals. Since most of the IEDs have the ferromagnetic properties, the magnetic field produced around the body by the IED can be detected by 3-axis fluxgate sensor array system. With these detected sensor data, the detection and localization of the IED can be computed by an appropriate method based on magnetic dipole model and nonlinear optimization algorithm. This paper studied respectively the properties of explosives and roads as target and environment to be detected. First, the gradient of total magnetic field was directly reconstructed from the magnetic field and the data of sensor array. In order to reduce the effects of the Earth’s magnetic field, the total gradient contraction was used to detect the IEDs. Second, the localization and magnetic moment parameters were searched in the rough by adopting the particle swarm optimization (PSO) algorithm and the precision ones were found by using steepest descent method. Simulation results show that the method mentioned achieves good effects, the maximum detection range of the sensor array can reach to 12 m for a 15 Am 2 target and the mean errors of localization and magnetic moment estimation are less than 0.16 m and 1 Am 2 respectively. Since the method need not know the IED’s magnetic moment in advance, it is adapt to battlefield environment. In addition, this detection method can be directly applied to solve the problem of detecting and localizing underwater IEDs.
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