This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra's algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.

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Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

A highly accurate indoor positioning under the effect of multipath reflections has been a prominent challenge for recent research. This paper proposes a novel indoor visible light communication (VLC) positioning model by connecting k-nearest neighbors (kNN) and random forest (RF) algorithms for reflective environments, namely, kNN-RF. In this fingerprint-based model, we first adopt kNN as a powerful solution to expand the number of input features for RF. Next, the importance rate of these features is ranked and the least effective one(s) may be removed to reduce the computation effort. Next, the training process using the RF algorithm is conducted. Finally, the estimation process is utilized to discover the final estimated position. Our simulation results show that this new approach improved the positioning accuracy, making it nearly five times better than other popular kNN algorithms.

Enhancing the accuracy of indoor visible light positioning systems with simple, real-time, and stable methods is one of the interesting challenges in recent research. In this paper, a relatively minor mean positioning error of 8 mm and a 42-52% improvement in computational time could be achieved within a real space of 1.2 m x 1.2 m x 1.2 m by transcending the serious limitations of the traditional knearest neighbors (KNN) algorithm. These disadvantages (slow execution time, high error formation) are a result of finding the nearest neighbors from all the fingerprints, averaging the Euclidean distances, and the excessive passivity of the K value. To overcome the above limitations of KNN, we proposed a maximum received signal strength recognition (MRR) technique and weighted optimum KNN (WOKNN) algorithm, which is a combination of optimum KNN (OKNN) and weighted KNN (WKNN). While MRR was used to reduce the computational time, WOKNN was used to solve the remaining problems. Specifically, OKNN was used to automatically determine the best number of nearest neighbors for each position in the area under consideration, and WKNN helped improve the errors that come from the Euclidean distance averaging process. Based on positive experimental results and a meaningful comparison with various versions of KNN, we demonstrated that the improved conventional KNN algorithm can achieve very high positioning accuracy and is totally suitable for several specific 2-D indoor positioning applications.

This paper presents a simulation of wind effects on a flying unmanned aerial vehicle (UAV) and a simulation of wind measurements taken from an airborne UAV. The wind acts as a disturbance on the flying UAV by altering its attitude and velocity. Therefore, the airborne measurement should be taken dynamically to reflect the instantaneous motion of a vehicle that is affected by wind. In order to understand and validate the algorithm that measures wind and the interaction between wind and the UAV, an investigation using simulation is extremely valuable. Considering real applications, this study implements modeling of the interaction between wind and a UAV from the perspective of the UAV's onboard sensors, and a wind measurement formula is clarified based on the established model. The results of the simulation confirm that, if an accurate measurements for angle-of-attack and sideslip are available, the wind can be accurately measured regardless of the control scheme used in flight.

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