Qidan Zhu

Robot Path Planning Based on Artificial Potential Field Approach with Simulated Annealing

The artificial potential field (APF) approach provides a simple and effective motion planning method for practical purpose. However, artificial potential field approach has a major problem, which is that the robot is easy to be trapped at a local minimum before reaching its goal. The avoidance of local minimum has been an active research topic in path planning by potential field. In this paper, we introduce several methods to solve this problem, emphatically, introduce and evaluate the artificial potential field approach with simulated annealing (SA). As one of the powerful techniques for escaping local minimum, simulated annealing has been applied to local and global path planning

Multisensor optimal information fusion white noise deconvolution filter

Using the modern time series analysis method and white noise estimation theory, under the linear minimal variance optimal information fusion criterion, a multisensor information fusion white noise deconvolution filter is presented for systems with correlated noises. The formula of computing covariances among filtering errors of sensors is presented, which can be applied to compute the optimal fused weighting matrices. Compared with the single sensor case, the accuracy of the fused filter is improved. It can be applied to signal processing in oil seismic exploration. A simulation example for information fusion Bernoulli-Gaussian white noise deconvolution filter shows its effectiveness.

Comments on "Asymptotic Backstepping Stabilization of an Underactuated Surface Vessel

The purpose of this short comment is to point out some mistakes in the considered paper. Furthermore, it is shown that the main results of the paper are not completed for the stabilization control of the ship system. For simplicity, all the symbols in this comment are the same as those in the paper under consideration.

A fuzzy-based collision avoidance approach for multi-robot systems

Multi-robot systems have been widely applied to various applications to perform a given task collaboratively and cooperatively. In a multi-robot environment, path-planning or collision avoidance is an imperative problem. For tackling this challenging problem, we mainly deal with the collision avoidance problem between robots and robots as well as between robots and obstacles in multi-robot systems. In this paper, the fuzzy reasoning based on a step-forward motion strategy is applied in making decision on motion of robots. The model of autonomous robot, the method of detecting dynamic speed of robot and the risk degree surrounding with a robot are discussed. The simulation results show that the fuzzy-based control strategies are effective and useful for making decisions on collision avoidance in multi-robot systems.

A new design scheme for discrete-time variable structure control systems

The design scheme of variable structure control for discrete-time systems is considered. Due to the shortcoming of discrete exponential reaching law which can not push the system state approach to zero but induce oscillation, a new discrete reaching law is presented to improve the reaching process of discrete exponential reaching law via using saturation function to replace sign function. The discrete-time variable structure control system designed by this reaching law can not only weaken the chattering and push the system state approach to zero gradually, but also keep the basic property of quasi-sliding mode which can cross the switching surface step by step. This method improves the reaching process and enhances the control quality of the discrete-time variable structure control system. The simulation results validate the feasibility and reasonability of this method.

Variable Structure Control Based on Fuzzy Law for Underactuated Robot Manipulators

This paper presents a stable sliding-mode control method for horizontal underactuated 2-DOF robot manipulators. First we define a two-level sliding surface for the whole system by divide the system into two subsystems. Then we use the Lyapunov feedback law to design a controller for the system with the fuzzy controller automatically tunes the slopes of sliding mode surface on line. The asymptotic stability of the sliding surfaces is established and proved theoretically in Lyapunov sense. Simulation results on the underactuated manipulators show that the whole system can be drive to the desire positions and get better system dynamic performance than the normal sliding mode control

Super-resolution image reconstruction for Omni-Vision based on POCS

The catadioptric omnidirectional sensor, called Omni-Vision, involving capture and automatic interpretation of images, depicts full horizontal panorama 360 degrees view of the surroundings. The field of view band can be easily transformed to a panoramic image and a converntional perspective image. However, it has an intrinsical disadvantage that the angular resolution Omni-Vision is lower than that of conventional video camera. In this paper, a super-resolution method for the Omni-Vision is proposed to reconstruct high resolution panoramic transformed images, in which consecutive images obtained by rotating motion of Omni-Vision are fused using the POCS (project on convex sets) algorithm. Experiment results are also provided to demonstrate the efficiency of the proposed method.

Solution for Forward Kinematics of 6-DOF Parallel Robot Based on Particle Swarm Optimization

The analysis of the forward kinematics is the foundation for studying other performances of the parallel robot. Making use of the property that it is easy to obtain the inverse kinematics of 6-DOF parallel robot, the forward kinematics of the 6-DOF parallel robot is transformed by using inverse kinematics results through training and learning. The nonlinear mapping from the joint variable space to the operation variable space for the platform is accomplished solving the location and posture. The BP neural network is used to solve the forward kinematics, and the particle swarm optimization is applied to train the neural network. Simulation results show that this approach can be used for the online control of parallel robot with faster computing speed and more accurate solution.

Modeling Pilot Behaviors Based on Discrete-Time Series During Carrier-Based Aircraft Landing

A pilot behavior model for carrier landing operations is established based on some realistic mechanisms and strategies. A discrete-time series is applied to model the pilot’s final actions. This pilot model adopts modular design. Furthermore, an information acquirement module, focus mode coordination strategy, and random error probability module are described in detail. Capability indices, including the information acquiring capability, response capability, action accuracy, and distraction capability, are introduced into this model to judge the flight ratings. In addition, this model contains a landing signal officer interface so that the pilot model can respond to the landing signal officer instructions. Finally, a simulation platform is built, on which this pilot model with different operating levels, a test pilot as well as a landing signal officer are emulated. The simulation results indicate that the outputs given by this model agree with the behavioral characteristics of realistic pilots; also, the ...

Modified Scale Invariant Feature Transform in omnidirectional images

The Scale Invariant Feature Transform, SIFT, is invariant to image translation, scaling, rotation, and is partially invariant to illumination changes. But, the time of features extraction and matching is huge, and the number of features is much larger then that is needed. To reduce the number of features generated by SIFT as well as their extraction and matching time, a modified approach based sampling is proposed. Mean-Shift algorithm is used in this modified SIFT to search local extrema points actively in scale space to improve the efficiency. It is demonstrated that the features extracted by modified SIFT are uniformly distributed in space, the time of feature extraction and matching is reduced obviously and the feature matching is accurate.