A method was proposed by combining the global and local path planning algorithms for the problems of slow iteration and not good path in mobile robot path planning under complex environment. Firstly, the synchronous bidirectional A ^* algorithm was used for the pheromone optimization of ant colony algorithm, and the transition probability and pheromone update mechanism of ant colony algorithm were improved, so that the global optimization of the algorithm was faster and the path length of the mobile robot was shortened. Furthermore, the static path was used to initialize the pigeon inspired optimization algorithm. Secondly, the improved pigeon inspired optimization algorithm was used for the local path planning of the mobile robot. The simulated annealing criteria were introduced to solve the local optimum problem, and the logarithmic S-type transfer function was used to optimize the step size of the number of pigeons, so as to better avoid the collision with dynamic obstacles. Finally, the cubic B-spline curve was used to smooth and replan the route. Simulation results indicate that the algorithm can generate smooth paths with short length and small evaluation value in both global static and local dynamic phases, and converge quickly, which is suitable for mobile robot to travel in dynamic and complex environments.

The missing of the sensing data is inevitable due to the inherent characteristic of Wireless Sensor Network (WSN),which affects various applications significantly. To solve the problem, an estimation algorithm for missing values based on attribute correlation of the sensing data was proposed. The multiple regression model was adopted to estimate missing values of attribute-correlated sensing data. Meanwhile, a data interleaved transmitting strategy was proposed to improve the robustness of the algorithm. The simulation results show that the proposed algorithm can estimate the missing values and is more accurate and reliable than some algorithms based on temporal and spatial correlation such as Linear interpolation Model (LM) algorithm and the traditional Nearest Neighbor Interpolation (NNI) algorithm.

This paper studied on the operational scheduling problem in a cross-docking center of a single receiving and a single shipping door with finite temporary storage. A dynamic programming model was built with the objective to minimize the costs including additional handing, temporary storage and truck replacement cost. A cultural algorithm with two layer evolutionary mechanism was proposed to solve the problem. The evolution of population space adopted genetic algorithm, and the belief space received good individual from population space to form knowledge which was used to guide evolution in turn. Numerical experiments under small and big scale situations prove the validity of proposed cultural algorithm.

To improve the accuracy and real-time performance of particle filter algorithm for tracking vision object, an improved algorithm in combination with mean shift and particle filter was proposed. Similar particles were clustered, and representative particles were iterated in each cluster by using mean shift algorithm. Then computation complexity was reduced by fewer mean shift iterative particles. Particle number and process noise distribution were adjusted adaptively based on tracking condition to improve tracking accuracy and reduce computation complexity. The experimental results prove the superiority of the proposed method, the average of each frame' operation time of this method is less than half of classic bybrid algorithm, and its computation complexity is also less than classic bybrid algorithm.

A fast border-detection algorithm named local border search algorithm was presented here, and applied to an on-line surface inspection system for cold rolled strips. Based on the last search result, a local search was used to position the cold rolled strip border and increased the algorithms speed greatly. Test with images collected from real world data shows that local border search algorithm is about 20 to 200 times faster than the traditional gray level gradient threshold algorithm, and can detect borders more accurately. The algorithm can also be used in the search of other image border.