In this paper, we propose a novel approach based on an unknown input method to synthesize the observer for a discrete time uncertain Takagi-Sugeno fuzzy system. The uncertainty bounds of the system can be unknown and the premise variables can be measurable or unmeasurable. Two main theorems are derived based on the noncommon quadratic Lyapunov function and feasible parameters are solved by the linear matrix inequality technique. Finally, a practical example is given to prove the effectiveness of the proposed method.

This paper proposes a new approach based on the unknown input method to synthesize the observer for polynomial Takagi–Sugeno fuzzy system with uncertainties. In this paper, the upper bounds of uncertainties are not given and the effect of uncertainties is eliminated without designing an extra controller. With the aids of the noncommon Lyapunov theory and Matlab's tools of the sum-of-square, a new observer is synthesized in which the observer form is completely different from the traditional observer forms reported in previous papers. The conditions for the observer synthesis are much relaxed and the complexity of the design process is reduced. Finally, two illustrative examples are presented to demonstrate the effectiveness of the proposed method.

In this study, we propose an assistive system for helping visually impaired people walk outdoors. This assistive system contains an embedded system—Jetson AGX Xavier (manufacture by Nvidia in Santa Clara, CA, USA) and a binocular depth camera—ZED 2 (manufacture by Stereolabs in San Francisco, CA, USA). Based on the CNN neural network FAST-SCNN and the depth map obtained by the ZED 2, the image of the environment in front of the visually impaired user is split into seven equal divisions. A walkability confidence value for each division is computed, and a voice prompt is played to guide the user toward the most appropriate direction such that the visually impaired user can navigate a safe path on the sidewalk, avoid any obstacles, or walk on the crosswalk safely. Furthermore, the obstacle in front of the user is identified by the network YOLOv5s proposed by Jocher, G. et al. Finally, we provided the proposed assistive system to a visually impaired person and experimented around an MRT station in Taiwan. The visually impaired person indicated that the proposed system indeed helped him feel safer when walking outdoors. The experiment also verified that the system could effectively guide the visually impaired person walking safely on the sidewalk and crosswalks.

This article presents a monocular vision-based algorithm for detecting obstacles and identifying obstacle-aware regions, developed to be used for collision avoidance by a multicopter. The first step of our algorithm is to predict a disparity image from a single-view image via implementing a deep encoder-decoder network. All pixels in this disparity prediction are then categorized as one of three classes, obstacle, road, or obstacle-free, by combining V-disparity analysis and a fuzzy inference system. For pixels belonging to obstacle objects, obstacle-aware regions are generated within the field of visual perception. To accommodate the safety margins of a multicopter, intermediate waypoints are then added to obtain a new flyable path that passes through an unknown environment safely. Experimental results verified the effectiveness of the detection of obstacles and the identification of obstacle-aware regions. The accuracy of disparity prediction and monocular depth estimation were quantitatively compared to support the feasibility of monocular vision in obstacle avoidance. Furthermore, the entire algorithm was successfully tested on a robotic platform, autonomously flying a hexacopter in an outdoor space with obstacles. In conclusion, the proposed monocular algorithm performs well for obstacle detection and depth estimation and is potentially an alternative to a binocular solution.

Detection of moving objects by unmanned aerial vehicles (UAVs) is an important application in the aerial transportation system. However, there are many problems to be handled such as high-frequency jitter from UAVs, small size objects, low-quality images, computation time reduction, and detection correctness. This paper considers the problem of the detection and recognition of moving objects in a sequence of images captured from a UAV. A new and efficient technique is proposed to achieve the above objective in real time and in real environment. First, the feature points between two successive frames are found for estimating the camera movement to stabilize sequence of images. Then, region of interest (ROI) of the objects are detected as the moving object candidate (foreground). Furthermore, static and dynamic objects are classified based on the most motion vectors that occur in the foreground and background. Based on the experiment results, the proposed method achieves a precision rate of 94% and the computation time of 47.08 frames per second (fps). In comparison to other methods, the performance of the proposed method surpasses those of existing methods.

This study presents a vision-based water color identification system designed for monitoring aquaculture ponds. The algorithm proposed in this system can identify water color, which is an important factor in aquaculture farming management. To address the effect of outdoor lighting conditions on the proposed system, a color correction method using a color checkerboard was introduced. Several candidates for water-only image patches were extracted by performing image segmentation and fuzzy inferencing. Finally, a deep learning-based model was employed to identify the color of these patches and then find the representative color of the water. Experiments at different aquaculture sites verified the effectiveness of the proposed system and its algorithm. The color identification accuracy exceeded 96% for the test data.