Energy-constrained sensor nodes are often deployed in remote, hilly, and hard-to-reach areas for civilian and military purposes. In such wireless sensor networks (WSNs), an unmanned aerial vehicle (UAV) can be used to collect data from the sensor nodes. Low-altitude UAVs can be utilized to reduce the energy consumption of WSNs by optimizing the data collection position. In this study, we designed an energy-efficient and fast data collection (EFDC) scheme in UAV-aided WSNs for hilly areas with the help of a UAV as a data mule. First, we proposed a central bias hybrid energy-efficient distributed clustering algorithm for grouping the sensors. Then, we applied a modified tabu search algorithm to optimize the UAV position for collecting data from a cluster. To achieve fast data collection, we developed the traveling salesman problem with the derived data collection positions and solved it by applying a modified genetic algorithm. Based on our simulation results, the proposed EFDC scheme outperforms the conventional ones in terms of energy consumption, scalability, control overhead, delay, and load balancing.

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Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Facial expressions are a form of nonverbal communication. Various studies have been done for the classification of these facial expressions. There is strong evidence for the universal facial expressions of eight emotions which include: neutral happy, sadness, anger, contempt, disgust, fear, and surprise. So it is very important to detect these emotions on the face as it has wide applications in the field of Computer Vision and Artificial Intelligence. These fields are researching on the facial emotions to get the sentiments of the humans automatically. In Robotics, emotions classification can be used to enhance human-robot interactions since the robot is capable of interpreting a human reaction [13]. In this paper, the emotion detection has been done in both real-time and static images. In this project, we have used the Cohn-Kanade Database (CK) and the Extended Cohn-Kanade (CK+) database, which comprises many static images 640 × 400 pixels and for the real-time using the webcam. The target expression for each sequence in the datasets are fully FACS (Facial action coding system) coded and emotion labels have been revised and validated [3]. So for emotion recognition initially we need to detect the faces by using HAAR filter from OpenCV in the static images or in the real-time videos. Once the face is detected it can be cropped and processed for further detection of facial landmarks. Then using facial landmarks the datasets are trained using the machine learning algorithm (Support Vector Machine) and then classified according to the eight emotions [1]. Using SVM we were getting the accuracy of around 93.7%. These facial landmarks can be modified for getting better accuracy.

Facial emotion recognition in real-time and static images

Recent years, unmanned aerial vehicles (UAVs) have been used to perform various tasks such as surveillance, monitoring, rescue, photography, and security. Quadcopter configuration of UAV is most common due to its simplicity, stability, and versatile controllability. Quadcopter landing is most crucial part of the overall operating process. It needs a lot of practice and effort for a safe landing. Therefore, a fuzzy logic-based safe landing system is proposed in this paper. This paper includes a landing system based on laser rangefinder, Arduino Mega MCU, and Pixhawk flight controller. The lookup table technique is used to implement fuzzy logic inside Arduino Mega. This technique takes very small execution time for data processing in fuzzy logic, which is essential for high-speed data processing and updating. Autonomous landing process can be triggered and override at any time using remote controller. Furthermore, various tests are performed on a quadcopter to verify the feasibility of proposed algorithm.

Fuzzy Logic-Based Robust and Autonomous Safe Landing for UAV Quadcopter

This paper proposes a novel search method for a swarm of quadcopter drones. In the proposed method, inspired by the phenomena of swarms in nature, drones effectively look for the search target by investigating the evidence from the surroundings and communicating with each other. The position update mechanism is implemented using the particle swarm optimization algorithm as the swarm intelligence (a well-known swarm-based optimization algorithm), as well as a dynamic model for the drones to take the real-world environment into account. In addition, the mechanism is processed in real-time along with the movements of the drones. The effectiveness of the proposed method was verified through repeated test simulations, including a benchmark function optimization and air pollutant search problems. The results show that the proposed method is highly practical, accurate, and robust.

Real-Time Swarm Search Method for Real-World Quadcopter Drones

This paper addresses the problem of trajectory tracking and control of a low-cost flying agent, equipped with a microcontroller for real-time navigation. A flying agent (aircraft-type quadcopter) has been designed and developed for steering control along a planned trajectory. A novel control scheme is developed using MATLAB/Simulink to ensure the right balance among pitch, roll and yaw of the flying agent wings. The proposed control scheme utilises a PID controller which regulates the error dynamics to stabilise the navigation. Various trajectories have been generated and verified through simulation studies as well as tested in a real model. The real-time experiments verify the manoeuvrability of the controller, which also checks the compatibility with hardware interfaces.

A Control Scheme for Navigation and Obstacle Avoidance of Autonomous Flying Agent

Angular maneuvering scheme along with standard flight operations such as taking-off, landing and hovering is proposed for a quadcopter with indoor or outdoor flying capabilities. This is achieved by simultaneously controlling the speed of the four rotors in order for the quadcopter to attain the correct orientation. The total thrust is determined using the inputs of altitude, pitch and roll angles. Then the thrust that the rotors must generate independently is obtained from the ratio of the angles and the calculated thrust for maintaining the input altitude. Voltage supply that is needed to spin the rotors at a certain RPM (Rotations per minute) is obtained to produce the thrust computed in the previous step. Moreover, the procedure on varying the thrust direction of rotors is also illustrated to perform the standard flight operations.

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Quadcopter Flight Dynamics

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M. Abdul Qadoos Bilal Khan

Papers

Quadcopter Flight Dynamics

Facial Expression Recognition Through Machine Learning