Strategy For Unmanned Aerial Vehicles Research Articles

Survey of Advanced Nonlinear Control Strategies for UAVs: Integration of Sensors and Hybrid Techniques.

This survey paper explores advanced nonlinear control strategies for Unmanned Aerial Vehicles (UAVs), including systems such as the Twin Rotor MIMO system (TRMS) and quadrotors. UAVs, with their high nonlinearity and significant coupling effects, serve as crucial benchmarks for testing control algorithms. Integration of sophisticated sensors enhances UAV versatility, making traditional linear control techniques less effective. Advanced nonlinear strategies, including sensor-based adaptive controls and AI, are increasingly essential. Recent years have seen the development of diverse sliding surface-based, sensor-driven, and hybrid control strategies for UAVs, offering superior performance over linear methods. This paper reviews the significance of these strategies, emphasizing their role in addressing UAV complexities and outlining future research directions.

Observer based attack detection and security control for UAVs against attacks on desired trajectory

This paper investigates an attack detection and security control strategy for unmanned aerial vehicles (UAVs) in the presence of malicious attacks. The detection, estimation, and compensation of the attack are the key points of the proposed scheme. This article considers that the desired trajectory sent by the ground control station (GCS) to the UAV is maliciously tampered with. First, a description of the UAV system and the desired trajectory attack is presented. The mathematical model is constructed by analyzing the characteristics of the desired trajectory attack. Secondly, an unknown input observer (UIO) is designed to generate the state estimation error. The attack detection framework is constructed by taking into account the state estimation error and the trajectory tracking error. Thirdly, an attack estimation observer with a prescribed H∞ performance is designed to compensate for the impact of attacks on the control system. A disturbance observer (DO) is used to compensate for the system disturbance. Finally, the simulation results are presented on Matlab and Gazebo to validate the effectiveness of the proposed security control strategy.

Strategies for Optimized UAV Surveillance in Various Tasks and Scenarios: A Review

This review paper provides insights into optimization strategies for Unmanned Aerial Vehicles (UAVs) in a variety of surveillance tasks and scenarios. From basic path planning to complex mission execution, we comprehensively evaluate the multifaceted role of UAVs in critical areas such as infrastructure inspection, security surveillance, environmental monitoring, archaeological research, mining applications, etc. The paper analyzes in detail the effectiveness of UAVs in specific tasks, including power line and bridge inspections, search and rescue operations, police activities, and environmental monitoring. The focus is on the integration of advanced navigation algorithms and artificial intelligence technologies with UAV surveillance and the challenges of operating in complex environments. Looking ahead, this paper predicts trends in cooperative UAV surveillance networks and explores the potential of UAVs in more challenging scenarios. This review not only provides researchers with a comprehensive analysis of the current state of the art, but also highlights future research directions, aiming to engage and inspire readers to further explore the potential of UAVs in surveillance missions.

Design of air-ground cooperative landing platform and autonomous landing strategy

This paper studies innovative design for landing platform design and autonomous landing strategies for unmanned aerial vehicles (UAVs). First, a self-leveling landing platform on a mobile unmanned ground vehicle (UGV) is designed, which can be maintained even when the UGV moves on uneven terrain. Then, with the cooperation of the UAV and the UGV, they are controlled to reach the same velocity in the horizontal direction, which helps avoid the UAV’s rollover caused by horizontal velocity difference. Meanwhile, a buffering strategy is designed to reduce the vertical velocity difference, which helps avoid a hard landing. Finally, a combined landing code is designed to improve the accuracy and success rate of UAV landing. Experiments are carried out to demonstrate the effectiveness of the proposed design of a self-leveling platform and autonomous landing strategies.

Development strategies for unmanned aerial vehicles in the construction industry

The paper provides research on the use of modern drones or unmanned aerial vehicles (UAVs) for effective work in the fields of architectural design, construction management and monitoring. The authors examined the theoretical foundations and practical features of modern unmanned aerial vehicles (UAVs) or drones. Statistics on the structure of the global UAV market are presented. The detailed description of the primary applications of drones in the construction sector is provided. The main advantages of new technologies over traditional ones are noted. The paper pays attention to the issues of increasing safety at construction sites. The introduction of innovative technologies is necessary for the development of the construction industry. The use of such technologies will reduce task completion times, improve work quality, improve safety standards and reduce costs. At the same time, the authors noted some shortcomings of modern unmanned technologies. In the conclusion of the paper, insights are presented regarding the future possibilities for integrating unmanned aerial vehicles in construction projects.

A Ground-Risk-Map-Based Path-Planning Algorithm for UAVs in an Urban Environment with Beetle Swarm Optimization

This paper presents a path-planning strategy for unmanned aerial vehicles (UAVs) in urban environments with a ground risk map. The aim is to generate a UAV path that minimizes the ground risk as well as the flying cost, enforcing safety and efficiency over inhabited areas. A quantitative model is proposed to evaluate the ground risk, which is then used as a risk constraint for UAV path optimization. Subsequently, beetle swarm optimization (BSO) is proposed based on a beetle antennae search (BAS) that considers turning angles and path length. In this proposed BSO, an adaptive step size for every beetle and a random proportionality coefficient mechanism are designed to improve the deficiencies of the local optimum and slow convergence. Furthermore, a global optimum attraction operator is established to share the social information in a swarm to lead to the global best position in the search space. Experiments were performed and compared with particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA), and BAS. This case study shows that the proposed BSO works well with different swarm sizes, beetle dimensions, and iterations. It outperforms the aforementioned methods not only in terms of efficiency but also in terms of accuracy. The simulation results confirm the suitability of the proposed BSO approach.

Autonomous Cooperative Guidance Strategies for Unmanned Aerial Vehicles During On-Board Emergency

This paper presents autonomous guidance strategies for both solo and group of unmanned aerial vehicles (UAVs), to be used when emergency conditions are encountered during mission. Fixed-wing UAVs equipped with batteries that serve as on-board power source are considered. They are also equipped with inertial and supervisory satellite-based navigation system. An emergency condition where limited available on-board power along with nonavailability of both ground command link and satellite-based navigation information is considered. We proposed two guidance strategies, look-lock-land and inject-contribute-exit for single- and multiple-UAV missions. An optimization model is developed to arrive at the maximum area that can be covered by spiral trajectory without violating on-board power constraints. In addition, we also proved that spiral trajectory acts like a circular highway for multiple-UAV missions. Algorithms to achieve look-lock-land, inject-contribute-exit and to compute Inertial Navigation Systems (INS) error correction factor are discussed. The performance of the proposed autonomous emergency guidance strategies is demonstrated on a sea-based oil-well-monitoring mission.

Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles.

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research.

Enhanced Potential Field-Based Collision Avoidance in Cluttered Three-Dimensional Urban Environments

With the various applications of unmanned aerial vehicles (UAVs), the number of UAVs will increase in limited airspace, leading to an increased risk collision. To reduce such potential risk, this work proposes a collision avoidance strategy for UAVs using an enhanced potential field (EPF) approach in cluttered three-dimensional urban environments. Using the EPF formulated in a two-dimensional environment, the avoidance maneuvers for both horizontal and vertical planes are generated by introducing rotation matrices, and these maneuvers are combined by applying a weighting factor. The numerical simulations with various meaningful scenarios are conducted to validate the performance of the proposed approach. To mimic practical situations, UAV dynamics and sensor limitations were considered. The simulation results show that the proposed approach provides an efficient, reliable, and collision-free path without local minima and unreachable goal issues.

Model predictive control for path planning of UAV group

This paper addresses a predictive control strategy for Unmanned Aerial Vehicles. The goal is to guarantee tracking capabilities with respect to a reference trajectory which is predefined using Gradient descent method. The proposed method provides effective performance validated through flight experiment using micro-UAVs.

Path planning strategy for unmanned aerial vehicles based on a grey wolf optimiser

Path planning strategy for unmanned aerial vehicles based on a grey wolf optimiser

Path planning strategy for unmanned aerial vehicles based on a grey wolf optimiser

Path planning strategy for unmanned aerial vehicles based on a grey wolf optimiser

A Risk-Aware Path Planning Strategy for UAVs in Urban Environments

This paper presents a risk-aware path planning strategy for Unmanned Aerial Vehicles in urban environments. The aim is to compute an effective path that minimizes the risk to the population, thus enforcing safety of flight operations over inhabited areas. To quantify the risk, the proposed approach uses a risk-map that associates discretized locations of the space with a suitable risk-cost. Path planning is performed in two phases: first, a tentative path is computed off-line on the basis on the information related to static risk factors; then, using a dynamic risk-map, an on-line path planning adjusts and adapts the off-line path to dynamically arising conditions. Off-line path planning is performed using riskA*, an ad-hoc variant of the A* algorithm, which aims at minimizing the risk. While off-line path planning has no stringent time constraints for its execution, this is not the case for the on-line phase, where a fast response constitutes a critical design parameter. We propose a novel algorithm called Borderland, which uses the check and repair approach to rapidly identify and adjust only the portion of path involved by the inception of relevant dynamical changes in the risk factor. After the path planning, a smoothing process is performed using Dubins curves. Simulation results confirm the suitability of the proposed approach.

Collision Avoidance Strategies for Unmanned Aerial Vehicles in Formation Flight

Collision avoidance strategies for multiple unmanned aerial vehicles (UAVs) based on geometry are investigated in this study. The proposed strategies allow a group of UAVs to avoid obstacles and separate if necessary through a simple algorithm with low computation by expanding the collision-cone approach to formation of UAVs. The geometric approach uses line-of-sight vectors and relative velocity vectors where dynamic constraints are included in the formation. Each UAV can determine which plane and direction are available for collision avoidance. An analysis is performed to define an envelope for collision avoidance, where angular rate limits and obstacle detection range limits are considered. Based on the collision avoidance envelope, each UAV in a formation determines whether the formation can be maintained or not while avoiding obstacles. Numerical simulations are performed to demonstrate the performance of the proposed strategies.

Analysis of design strategies for unmanned aerial vehicles using co-simulation

Designing critical embedded systems, like UAVs is not a trivial task because it brings the challenge of dealing with the uncertainty that is inherent to this type of systems, e.g., winds, GPS uncertainty, etc. Simulation and verification tools that provide a level of confidence can help design such systems and increase the safety of specified cyber-physical systems before deployment. This paper presents a framework for evaluating flight strategies of UAVs. Our framework is constructed by integrating, using high-level architecture, Ptolemy, a high level specification tool, and SITL/Ardupilot, a domain specific UAV simulator. It allows to evaluate flight strategies under the presence of uncertainty, such as winds, with a level of confidence by constructing a sufficiently large number of simulations. Its effectiveness is demonstrated by testing two different flight strategies in two scenarios under different wind intensities. We measure the flight quality providing quantitative information about the quality of the tested flight strategy, such as distance traveled, with a confidence of 95% and error of 8%.

Real-Time First-Order Guidance Strategies for Trajectory Optimization Through Wind Energy Utilization

This paper presents real-time guidance strategies for unmanned aerial vehicles that can be used to enhance their flight endurance by using in situ measurements of wind speeds and wind gradients. In these strategies, periodic adjustments would be made in the airspeed and/or heading angle command for the unmanned aerial vehicle to minimize a projected power requirement at some future time. In this paper, unmanned aerial vehicle flights are described by a three-dimensional dynamic point mass. Onboard closed-loop trajectory tracking logics that follow airspeed vector commands are modeled using the method of feedback linearization. A generic wind field model is assumed that consists of a constant term plus terms that vary sinusoidally with respect to the location. To evaluate the benefits of these strategies in enhancing unmanned aerial vehicle flight endurance, a reference strategy is introduced in which the unmanned aerial vehicle would seek to follow the desired airspeed in a steady level flight under zero wind. A performance measure is defined as the average power consumption both over a specified time interval and over different initial heading angles of the unmanned aerial vehicle. A relative benefit criterion is then defined as the percentage improvement of the performance measure of a proposed strategy over that of the reference strategy. Extensive numerical simulations are conducted. Results demonstrate the benefits and trends of power savings of the proposed real-time guidance strategies.

Feature article: an advanced sense and collision avoidance strategy for unmanned aerial vehicles in landing phase

Unmanned Aerial Vehicles (UAVs) become promising in different civilian and military applications due to their lower cost and greater flexibility in comparison to manned aircraft. However, the growing diversity of flight makes UAVs vulnerable to mid-air collisions. To ensure the safety for manned aircraft, human pilots are responsible for detecting intruders in airspace and performing appropriate maneuvers to avoid collisions [1]. Unlike manned aircraft with human pilots involved, UAVs must be equipped with Sense and Avoid (SAA) systems to guarantee the flight safety [2]. Thus, SAA systems play an important role in merging UAVs into the National Airspace System. As illustrated in Figure 1, an SAA scheme is composed of four units, which are sensing, conflict detection, collision avoidance, and flight controller, respectively.

OQTAL: Optimal quaternion tracking using attitude error linearization

The use of quaternions or quaternion error attitude control strategies for unmanned aerial vehicles (UAVs) is commonplace. Quaternion tracking error control is usually presented in rather theoretical works, where the control algorithm is almost exclusively chosen to be a suboptimal one. However, the application of optimal control techniques is usually associated to simplified attitude models frequently aimed at solving real-life problems. The work presented in this paper aims to formally merge the development of a complete theoretical quaternion error model with an optimal control strategy. Moreover, the application of optimal control algorithms to a fully defined quaternion error state-space model and the validation of the same in a real-time experimental setup is the focus of this research. The result is a novel controller named Optimal Quaternion Tracking of Attitude Error Linearization (OQTAL). The paper provides a comprehensive proof of stability, full simulation validation for a planetary landing gravity turn trajectory, and evidence of repeatable experimental work for a real-time quadrotor UAV on a motion capture testbed. OQTAL is compared with proven optimal forms of (PID) proportional-integral-derivative control and linear quadratic regulator control and is shown to have a 10%–20% reduction in error for the experimental setup trajectory tracking trials and an even larger tracking error reduction in the gravity turn simulation trials. Furthermore, for close tracking conditions, OQTAL behaves almost like a linear and time-invariant system, therefore requiring limited computation time for performing the trajectory tracking.

A Cooperative Search Strategy for Moving Targets Using UAV Swarms

This paper designs a cooperative strategy for unmanned aerial vehicles (UAVs) searching for moving and possibly evading targets. By arranging UAVs into an efficient flight configuration, they optimize their integrated sensing capability, and are thus capable of searching smart targets than uncooperative UAVs. If a UAV is shot down, the team adapts to continue the mission with surviving assets. Simulation results show that our proposed strategy can achieve superior search performance.