Quadrotor Trajectory Tracking Using Linear and Nonlinear Model Predictive Control

Accurate trajectory tracking is an essential characteristic for the safe navigation of a quadrotor in cluttered or disturbed environments. In this paper, we present in detail two state-of-the-art model-based control frameworks for trajectory tracking: the Linear Model Predictive Controller (LMPC) and the Nonlinear Model Predictive Controller (NMPC). Additionally, the kinematic and dynamic models of the quadrotor are comprehensively described. Finally, a simulation system is implemented to verify feasibility, demonstrating the effectiveness of both controllers.

Recognition of 26 Degrees of Freedom of Hands Using Model-based approach and Depth-Color Images

In this study, we present an model-based approach to recognize full 26 degrees of freedom of a human hand. Input data include RGB-D images acquired from a Kinect camera and a 3D model of the hand constructed from its anatomy and graphical matrices. A cost function is then defined so that its minimum value is achieved when the model and observation images are matched. To solve the optimization problem in 26 dimensional space, the particle swarm optimization algorimth with improvements are used. In addition, parallel computation in graphical processing units (GPU) is utilized to handle computationally expensive tasks. Simulation and experimental results show that the system can recognize 26 degrees of freedom of hands with the processing time of 0.8 seconds per frame. The algorithm is robust to noise and the hardware requirement is simple with a single camera.

Using multiple sensors for autonomous mobile robot navigation

This paper presents the use of multi-sensor measurement system to guide autonomous mobile robot in the house. The system allows the 3D image acquisition to global mapping, and algorithms to reduce the dimensionality of images to 2D global map navigation, trajectory design approach using the Lyapunov function method and avoid obstacles by the potential energy can also be presented. Also, sensor integrated method based on extended Kalman filter allows us to identify the exact location and orientation of the robot in the presence of interference from the environment.