A method for path planning of autonomous robot using A* algorithm

Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles used for military, homeland security, and disaster-monitoring missions involving intelligence, surveillance, or reconnaissance (ISR). The benefits include increased time on station and range as compared to electric-powered unmanned aerial vehicles and reduced acoustic and thermal signatures not available with gasoline-powered unmanned aerial vehicles. A conceptual design of a small unmanned aerial vehicle with a parallel hybrid-electric propulsion system, the application of a rule-based controller to the hybrid-electric system, and simulation results are provided. The two-point conceptual design includes an internal combustion engine sized for cruise speed and an electric motor and lithium-ion battery pack sized for endurance speed. A rule-based controller based on ideal operating line concepts is applied to the control of the parallel hybrid-electric propulsion system. The energy use for the 13.6 kg (30 Ib) hybrid-electric unmanned aerial vehicle with the rule-based controller during one-hour and three-hour ISR missions is 54% and 22% less, respectively, than for a four-stroke gasoline-powered unmanned aerial vehicle.

In this paper, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system.

This book provides fundamental principles, design procedures, and design tools for unmanned aerial vehicles (UAVs) with three sections focusing on vehicle design, autopilot design, and ground system design. The design of manned aircraft and the design of UAVs have some similarities and some differences. They include the design process, constraints (e.g., g-load, pressurization), and UAV main components (autopilot, ground station, communication, sensors, and payload). A UAV designer must be aware of the latest UAV developments; current technologies; know lessons learned from past failures; and they should appreciate the breadth of UAV design options. The contribution of unmanned aircraft continues to expand every day and over 20 countries are developing and employing UAVs for both military and scientific purposes. A UAV system is much more than a reusable air vehicle or vehicles. UAVs are air vehicles, they fly like airplanes and operate in an airplane environment. They are designed like air vehic...

URLLC in UAV-enabled multicasting systems: A dual time and energy minimization problem using UAV speed, altitude and beamwidth

Traditional bulky and complex control devices such as remote control and ground station cannot meet the requirement of fast and flexible control of unmanned aerial vehicles (UAVs) in complex environments. Therefore, a data glove based on multi-sensor fusion is designed in this paper. In order to achieve the goal of gesture control of UAVs, the method can accurately recognize various gestures and convert them into corresponding UAV control commands. First, the wireless data glove fuses flexible fiber optic sensors and inertial sensors to construct a gesture dataset. Then, the trained neural network model is deployed to the STM32 microcontroller-based data glove for real-time gesture recognition, in which the convolutional neural network-Attention mechanism (CNN-Attention) network is used for static gesture recognition, and the convolutional neural network-bidirectional long and short-term memory (CNN-Bi-LSTM) network is used for dynamic gesture recognition. Finally, the gestures are converted into control commands and sent to the vehicle terminal to control the UAV. Through the UAV simulation test on the simulation platform, the average recognition accuracy of 32 static gestures reaches 99.7%, and the average recognition accuracy of 13 dynamic gestures reaches 99.9%, which indicates that the system’s gesture recognition effect is perfect. The task test in the scene constructed in the real environment shows that the UAV can respond to the gestures quickly, and the method proposed in this paper can realize the real-time stable control of the UAV on the terminal side.

Small Unmanned Aerial Vehicle(UAV) is well suited for missions that are dangerous to perform with human pilots. Control effort in the form of Automatic Flight Control System(AFCS) for achieving the desired flight profile against any unexpected disturbance or parameter variations is needed in such UAV's. Most of the UAV dynamics uses Euler rate method for axis transformation. Alternate computational methods have been evolved to compute and derive parameters from the available sensors. This paper attempts to employ quaternion based closed loop attitude control. The major problem encountered with the axis transformation with Euler rates is the singularity at 90° theta which can be directly removed when quaternion based control is employed. One of the major advantages in employing quaternion based closed loop control is that, it is independent of the error due to sensors. Hence this paper attempts to employ quaternion based control in a low speed subsonic UAV platform. The main aim of this paper is to introduce quaternion based feedback approach into the nonlinear UAV simulation model with Linear Quadratic Regulator (LQR) and Sliding Mode Controller (SMC).Unexpected disturbance and parameter variation can be tolerated by using robust fuzzy SMC. Simulation results show the effectiveness of the projected method.

Energy optimization for full-duplex wireless-powered IoT networks using rotary-wing UAV with multiple antennas

This paper presents the conceptual design of a multi-rotor unmanned aerial vehicle (UAV) based on an axiomatic design. In most aerial vehicle design approaches, design configurations are affected by past and current design tendencies as well as an engineer's preferences. In order to design a systematic design framework and provide fruitful design configurations for a new type of rotorcraft, the axiomatic design theory is applied to the conceptual design process. Axiomatic design is a design methodology of a system that uses two design axioms by applying matrix methods to systematically analyze the transformation of customer needs into functional requirements (FRs), design parameters (DPs), and process variables. This paper deals with two conceptual rotary wing UAV designs, and the evaluations of tri-rotor and quad-rotor UAVs with proposed axiomatic approach. In this design methodology, design configurations are mainly affected by the selection of FRs, constraints, and DPs.

In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. The proposed design aims to maximize the achievable sum rate of all networks by jointly optimizing UAV placement; resource management strategies; transmit power allocation; and ARIS reflection coefficients, subject to backhaul constraints and power budget limitations in the ARIS system. The resulting optimization problem is highly non-convex, posing significant challenges. To tackle this, we decompose the problem into three interrelated sub-problems and apply inner approximation (IA) techniques to handle the non-convexities within each sub-problem. Moreover, a comprehensive alternating optimization framework is proposed to implement an iterative solution for the sub-problems. Simulation results demonstrate that the proposed algorithm achieves approximately 59% improvement in the average sum rate, substantially enhancing overall network reliability compared to existing benchmark schemes.

The Unmanned Aerial Vehicle (UAV) has special dynamical characteristics in ground motion different from that of flight in the airborne. The forces and moments on the ground to the UAV differ during taxiing, according to the different mechanical and aerodynamic characteristics. An all-states nonlinear model is established by studying on a sample UAV. Rotation speed and lift off speed is determined, finally a control law including longitudinal and lateral control for automatic take-off is designed. Results of simulation show that the model and control scheme can not only join the ground taxiing and flight in the airborne smoothly, but also has a high value in realizing engineering project.

In this paper, considering a seamless control of an unmanned aerial vehicle (UAV) by a ground base station (GBS), the energy consumption of the UAV is minimized in the downlink communication from the GBS with multiple antennas to the UAV with two antennas. By exploiting the benefit of a space–time line code (STLC) scheme that does not require training for full channel estimation at the receiver, i.e., the UAV, we employ the STLC scheme and design the passband bandwidth. Precisely, the number of symbols within the given frame duration is designed to reduce the total energy consumption at the UAV for the given data volume. With the designed frame, we numerically verify that the proposed STLC-based UAV consumes less energy than a UAV employing the maximum eigen beamforming scheme when optimized in terms of the received signal-to-noise ratio.

ABSTRACT-This research work focuses on the application of Quantum Reinforcement Learning (QRL) to improve path planning for autonomous robots. Traditional path planning techniques are prone to failure in handling the complexity of uncertain and dynamic environments, but QRL leverages the power of quantum computing to exploit the principles of superposition and parallelism to improve decision-making and optimization processes. By integrating QRL with existing reinforcement learning techniques, the proposed technique quickens the pace of learning processes and improves the quality of path planning to enable robots to navigate around obstacles more effectively with shorter travel time and lower energy costs. Simulation results show that QRL outperforms traditional techniques in convergence speed and optimality of path solutions and offers a promising solution for practical autonomous navigation. Keywords: Dynamic Environment, Path Planning, Autonomous Navigation, Obstacles avoidance.

In current day’s Unmanned Aerial Vehicle (UAV) are widely used in every field, almost from military till the commercial purpose. Usage of UAV has decreased the burden on the human, where the manpower and risks during critical conditions (war fields) are reduced. Therefore the demand for the development of the unmanned aerial Vehicle is high. Interpreting the conceptual design data of UAV is difficult because of lack of availability of their data sheets. This paper is addressed to develop a conceptual design process of high-performance UAV, which carries a maximum payload of about 300kg and can travel for the maximum range 900km/hrs. For about 50 hours of maximum endurance. Where these three parameters are considered as the main requirements. By drawing constraint diagram, feasible design space for the aircraft was frozen; using which initial sizing of the wing, wing airfoil selection, and a suitable power-plant selection was done. Fuselage design was carried out looking at the available literature on the existing aircrafts. Propeller design was done to match the thrust requirements obtained from the constraint diagram. Empennage design was done to achieve the desired static margin of the aircraft. This process completes the conceptual design of UAV, where designed aircraft meets the requirement. The outcome of this paper enhances the understanding of the conceptual design process for the academicians as well as the researchers.

In this paper, we describe the design and performance of a prototype multi-rotor unmanned aerial vehicle (UAV) platform featuring autonomous flying based on an inertial measurement unit (IMU) for use in bluetooth communication environments. Although there has been a fair amount of study on free-flying UAVs with multiple rotors, the more recent trend has been to outfit hexarotor UAVs with gimbals to support various services. This paper introduces the hardware and software systems for very compact and autonomous hexarotors, which can be used for search & rescue and surveillance missions without external assistance systems like ground station computers, high-performance remote control devices, or vision systems. This study included the construction of the test hexarotor platform, the implementation of an IMU, mathematical modeling, and simulation of the UAV. Furthermore, the hexarotor UAV with the implemented IMU was connected to a microcontroller unit (ARM-cortex) board. The microcontroller was able to control the rotational speed of the rotors and used the IMU measurements as input signals. A control simulation and experiment with the real system were implemented for the test platform, and the results were evaluated and compared.

Design complexities of trending UAVs and the operational harsh environments necessitates Control Law formulation utilizing intelligent techniques that are both robust, model-free and adaptable. In this research, an intelligent control architecture for an Unmanned Aerial Vehicle (UAV) having an unconventional inverted V-tail design, is presented. Due to unique design of the vehicle strong roll and yaw coupling exists, making the control of vehicle challenging. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. This stabilization and tracking controller for the UAV achieves the goal of an optimal flight path and exhibits satisfactory performance. The paper illustrates the application of modified DDPG algorithm towards the design, while the performance of the resulting controller is assessed in simulation using dynamic model of the vehicle. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory and another DRL method named Proximal Policy Optimization (PPO). Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significance utility to airborne applications.