Xiaoping Fan

Optimal path planning for mobile robots based on intensified ant colony optimization algorithm

Optimal path planning for mobile robots plays an important role in the field of robotics. At present, there are many advanced algorithms used to solve this optimal problem. However, for those algorithms, it is very difficult to solve some path planning problems containing certain constraint conditions due to the complex background environment. Based on the intensified ant colony optimization algorithm, the path planning optimization is carried out with numerical analysis and computations. The simulation results are satisfactory. In this paper, for the first time, intensified ant colony optimization algorithm is used to solve path planning for mobile robots which is a continuous function optimization problem containing constraint conditions. It also provides a new solving approach to the similar problems.

Integrated Optimization of Trajectory Planning for Robot Manipulators Based on Intensified Evolutionary Programming

Optimal trajectory planning for robot manipulators is always the hot spot in the research field of robotics. The performance indexes used in optimal trajectory planning are classified into two main categories. One is called optimum traveling time. The other is called optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. Unfortunately, there are few planning algorithms designed to satisfy two performance indexes simultaneously. There are some deficiencies in the existing integrated optimization algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators

Design of an adaptive fuzzy logic controller for traffic signals

An adaptive fuzzy logic signal controller (AFC) is presented for the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection by a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the adaptive module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is developed to describe the interactions of traffic flow between the adjacent intersections. So the AFC combined with the WCC can be applied in a road network. Evaluation of the performance on a real traffic scenario has been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

MTTP for robot manipulators based on intensified evolutionary programming and its application in PVR environment

The minimum time trajectory planning (MTTP) of robot manipulator plays an important role in the field of robotics. However, it is very difficult to reach the optimal goal due to the nonlinearities in the mathematic model and the strong couplings in robot dynamics. Based on the intensified evolutional programming kinematic approach, the trajectory planning optimization is carried out with numerical analysis and computations. Meanwhile, a kind of graphic simulation software is developed by using the new optimal algorithm based on projective virtual reality (PVR) technology. The graphic simulation and experimental results show that the intuitive operability, control, and supervision can be realized effectively in the virtual world, and the precision of projective operability is greatly enhanced.

A Fast Map Merging Algorithm in the Field of Multirobot SLAM

In recent years, the research on single-robot simultaneous localization and mapping (SLAM) has made a great success. However, multirobot SLAM faces many challenging problems, including unknown robot poses, unshared map, and unstable communication. In this paper, a map merging algorithm based on virtual robot motion is proposed for multi-robot SLAM. The thinning algorithm is used to construct the skeleton of the grid maps empty area, and a mobile robot is simulated in one map. The simulated data is used as information sources in the other map to do partial map Monte Carlo localization; if localization succeeds, the relative pose hypotheses between the two maps can be computed easily. We verify these hypotheses using the rendezvous technique and use them as initial values to optimize the estimation by a heuristic random search algorithm.

Heuristic Search Assisted Active Localization for Mobile Robot

A heuristic search assisted active localization method is proposed.The algorithm clusters the particles by using adaptive particle clustering algorithm.Then,path planning trees and solution space trees are constructed respectively.Priories of all the nodes in the solution space trees are calculated according to the priority evaluation function,and the problem of path search is solved by the priority queue-type branch-and-bound method.Finally,a localizing accuracy active enhancing method is presented to solve the particle divergence problem in a single particle cluster.Simulation experiments validate the feasibility of the methods.

Identification of countercurrent rare earth extraction process based on nonlinear state-space models

The system identification problem of rare earth extraction process, which is based on local linear weighted state-space models, is considered in this paper. The parameter is determined by the minimization of output error cost function. In addition, the nonuniqueness of the fully parameterized state-space systems is considered and the parameter update is restricted to the direction which is perpendicular to the tangent space to the manifold of observationally input-output equivalent local linear weighted state-space systems. Moreover, the convergence speed of the proposed algorithm is also given. Finally, real industrial experiment of rare earth extraction process is implemented and the results show the efficiency of the proposed method.

Subspace identification of countercurrent rare earth extraction process based on nonlinear state-space models

The subspace identification problem of rare earth extraction process, which is based on nonlinear state-space models, is considered in this paper. The nonlinear part of the system model is approximated by the radial basis functions. The parameters are estimated in two steps. Firstly, The system matrices are determined by subspace identification method. Secondly, the parameters of the radial basis functions are computed by optimization of output error cost function. Real industrial experiment of rare earth extraction process is implemented and the results show the efficiency of the proposed method.

Research on Optimized Combined Cosine Windows with Maximum Side Lobe Decay for Harmonic Analysis

Because of all kinds of factors, it is very difficult to achieve coherent sampling and integrated-cycle truncation when power grid harmonics are analyzed. But there inevitably exists spectral leakage when Fourier transform is directly adopted in incoherent sampling mode, and the accuracy of harmonic analysis will be seriously affected. To reduce spectral leakage, it’s a very important measure to weight the sampling data with appropriate window, and combined cosine windows are a series of useful windows for power grid harmonic analysis. Its coefficients are worth of being optimized so as to more notably reduce spectral leakage. To aim at these things, this paper puts forward general principles to optimize combined cosine windows, and deduces the optimized combined cosine windows with maximum side lobe decay. In the incoherent sampling mode, these optimized windows can make side lobes decay with the maximum rate, so they can significantly reduce spectral leakage and can help to realize high-precision power grid harmonics measurement and analysis.

Improve template parameter estimation for pseudo-periodic signal processing and application

In order to process and analyze pseudo-periodic signals online in time-domain for the system with limited hardware resource, this paper presents a simplified method of template function parameters estimation. Treating the pseudo-periodic signals in short time as periodic, the method selects Fourier series as template and the initial parameters are estimated by fitting off-line. The base frequency of the template is determined from the periods of signal measured and the multiples of the base frequency are treated as the estimations of high-order frequency. Adopting square sum of the matching error between measurement and template as cost function, the method estimates or calculates the characteristic parameters of the pseudo-periodic signal by adjusting online amplitude and order of the template series. The experiment on processing radial artery pulse pressure measured by PVDF sensor online shows that the improved method is effective and helpful to simplify pulse wave detecting system.