Purpose-The aim of this paper is to propose a robust robot fuzzy logic proportional-derivative (PD) controller for trajectory tracking of autonomous nonholonomic differential drive wheeled mobile robot (WMR) of the type Quanser Qbot. Design/methodology/approach-Fuzzy robot control approach is used for developing a robust fuzzy PD controller for trajectory tracking of a nonholonomic differential drive WMR. The linear/angular velocity of the differential drive mobile robot are formulated such that the tracking errors between the robot's trajectory and the reference path converge asymptotically to zero. Here, a new controller zero-order Takagy-Sugeno trajectory tracking (ZTS-TT) controller is deduced for robot's speed regulation based on the fuzzy PD controller. The WMR used for the experimental implementation is Quanser Qbot which has two differential drive wheels; therefore, the right/left wheel velocity of the differential wheels of the robot are worked out using inverse kinematics model. The controller is implemented using MATLAB Simulink with QUARC framework, downloaded and compiled into executable (.exe) on the robot based on the WIFI TCP/IP connection. Findings-Compared to other fuzzy proportional-integral-derivative (PID) controllers, the proposed fuzzy PD controller was found to be robust, stable and consuming less resources on the robot. The comparative results of the proposed ZTS-TT controller and the conventional PD controller demonstrated clearly that the proposed ZTS-TT controller provides better tracking performances, flexibility, robustness and stability for the WMR. Practical implications-The proposed fuzzy PD controller can be improved using hybrid techniques. The proposed approach can be developed for obstacle detection and collision avoidance in combination with trajectory tracking for use in environments with obstacles. Originality/value-A robust fuzzy logic PD is developed and its performances are compared to the existing fuzzy PID controller. A ZTS-TT controller is deduced for trajectory tracking of an autonomous nonholonomic differential drive mobile robot (i.e. Quanser Qbot).
This paper investigates formation control of multiple nonholonomic differential drive wheeled mobile robots (WMRs). Assume the communication between the mobile robots is possible where the leader mobile robot can share its state values to the follower mobile robots using the leader-follower notion. Two approaches are discussed for controlling a formation of nonholonomic WMRs. The first approach is consensus tracking based on graph theory concept, where the linear and angular velocity input of each follower are formulated using first order consensus protocol, such that the heading angle and velocity of the followers are synchronized to the corresponding values of the leader mobile robot. The second is l- approach (distance angle) that is developed based on Lyapunov analysis, where the linear and angular velocity inputs of each follower mobile robot are adjusted such that the followers keep a desired separation distance and deviation angle with respect to the leader robot, and the overall system is asymptotically stable.The aim of this paper is to compare the performances of the presented methods for controlling a formation of wheeled mobile robots with matlab simulations.
The creation of digital three dimensional (3D) spatial models for the real scenes using image collections is one of the interesting applications of computer vision. In this paper we present some experimental results for creating textured 3D models from image collections using open source software packages (i.e. VisualSFM, CMVS, SURE, MeshLab, Cloud Compare). The images can be picked up with different types of cameras, or using the different imaging systems like UAVs and Satellites. At first, we use VisualSFM which is a robust Structure from Motion software estimating the calibration parameters of all the images, and a sparse 3D point cloud. We present two alternative softwares for multi view dense stereo reconstruction (CMVS and SURE). CMVS and SURE are effective tools and can operate on the common desktop PCs. The obtained results with CMVS and SURE are visualized with Meshlab and Cloud Compare respectively. Again we used MeshLab for mesh generation and texture mapping based on Poisson's filter for surface reconstruction and texturing tools available in Meshlab.
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