Cai Ze-su

A novel approach based on evolutionary game theoretic model for multi- player pursuit evasion

In this paper, we consider a novel Three Hierarchical decomposition approach for Multi-Player Pursuit Evaders (MPPE) game. In multi-player pursuit evasion game, hierarchical framework is applied widely in order to decompose the original complicated multi-player game into multiple small scale games. In this paper, we first study the number of pursuers which necessitates; the capture condition and the time of all evaders have been captured. Then, describe the Distributed Task Assignment Stage Based on dynamic Coalition Formation. Last, a novel multi-agent Q-learning approach based on Evolutionary Game Theoretic model is used for pursue. Experimental results obtained on two different environments of a well-known pursuit domain show the effectiveness and robustness of the proposed Hierarchical architecture and learning approach.

Formation Control and Obstacle Avoidance for Multiple Robots Subject to Wheel-Slip

An adaptive formation control law for non-holonomic dynamic robots based on an artificial potential function method in the presence of lateral slip and parametric uncertainties is presented to organize multiple robots into formation. It is formulated to achieve the smooth control of the translational and rotational motion of a group of mobile robots while keeping a prescribed formation and avoiding inter-robot and obstacle collisions. In order to improve the formation control method effectively and reduce the distortion shape, the virtual leader-following method is proposed for every robot and an improved optimal assignment algorithm is used to solve multi-targets optimal assignment for the formation problem. Simulation results are provided to validate the theoretical results.

A robust vision-based method for staircase detection and localization

Abstract A robust vision-based staircase identification method is proposed, which comprises 2D staircase detection and 3D staircase localization. The 2D detector pre-screens the input image, and the 3D localization algorithm continues the task of retrieving geometry of the staircase on the reported region in the image. A novel set of principal component analysis-based Haar-like features are introduced, which extends the classical Haar-like features from local to global domain and are extremely efficient at rejecting non-object regions for the early stages of the cascade, and the Viola–Jones rapid object detection framework is improved to adapt the context of staircase detection, modifications have been made on the scanning scheme, multiple detections integrating scheme and the final detection evaluation metrics. The V-disparity concept is applied to detect the planar regions on the staircase surface and locate 3D planes quickly from disparity maps, and then, the 3D position of staircase is localized robustly. Finally, experiments show the performance of the proposed method.