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L2 Gain And Passivity Techniques In Nonlinear Control

Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Output Feedback Control of a Quadrotor UAV Using Neural Networks

Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

In this paper, we consider the structure inspection problem using a miniature unmanned aerial vehicle (UAV). The influence of the wind on the UAV behavior and onboard energy limitations are important parameters that must be taken into account in the structure inspection problem. To tackle these problems, we derive three methods to inspect a structure. First, we develop a Zermelo-Traveling Salesman Problem (TSP) method to compute the optimal route to inspect a simple virtual structure. Second, we derive a method that combines meshing techniques with the Zermelo-TSP method. In this approach, the inspection coordinates for the interest points are obtained automatically by means of a meshing algorithm, then, the Zermelo-TSP method is used to compute the time-optimal route to inspect all the interest points in minimal time. Finally, we derive a method for structure inspection based on the Zermelo-Vehicle Routing Problem (VRP). These methods have been validated in a simulated environment.

UAV Path Planning for Structure Inspection in Windy Environments

This brief presents a trajectory planning algorithm for aerial vehicles traveling in 3-D space while avoiding obstacles. The nature of the obstacles can be, for example, radar detection areas, cooperating and non-cooperating vehicles, and so on. Thus, it is a complex trajectory planning problem. The proposed planner is based on the optimal rapidly exploring random tree (RRT*) algorithm. Artificial potential fields are combined with the RRT* algorithm to accelerate the convergence speed to a suboptimal solution by biasing the random state generation. The performance of this framework is demonstrated on a complex missile application in a heterogeneous environment. Indeed, since the air density decreases exponentially with altitude, the maneuverability of the aerial vehicle depending on aerodynamic forces also decreases exponentially with altitude. To face this problem, the shortest paths of Dubins-like vehicles traveling in a heterogeneous environment are used to build the metric. In the simulation results, this framework can find the first solution with fewer iterations than the RRT and the RRT* algorithm. Moreover, the final solution obtained within a given number of iterations is closer to an optimal solution regarding the considered criterion.

3-D Trajectory Planning of Aerial Vehicles Using RRT*

In this paper, a quadrotor optimal coverage planning approach in damaged area is considered. The quadrotor is assumed to visit a set of reachable points following the shortest path while avoiding the no-fly zones. The problem is solved by using a two-scale proposed algorithm. In the first scale, an efficient tool for cluttered environments based on optimal Rapidly-exploring Random Trees (RRT) approach, called multi-RRT∗ Fixed Node (RRT∗FN), is developed to define the shortest paths from each point to their neighbors. Using the pair-wise costs between points provided by the first-scale algorithm, in the second scale, the overall shortest path is obtained by solving the Traveling Salesman Problem (TSP) using Genetic Algorithms (GA). Taking into consideration the limited onboard energy, a second alternative based on the well-known Vehicle Routing Problem (VRP) is used. This latter is solved using the savings heuristic approach. The effectiveness of this proposed two-scale algorithm is demonstrated through numerical simulations and promising results are obtained.

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Quadrotor-UAV optimal coverage path planning in cluttered environment with a limited onboard energy

Nonlinear internal model control applied to VTOL multi-rotors UAV

Motion control design plays a crucial role in autonomous vehicles. Mainly, these systems operate in conditions of under-actuation, which make the control a serious task especially in presence of practical constraints. The main objective within this paper is to ensure the tracking of 3D reference trajectory overcoming some of the issues related to the control of multi-rotor vehicles (such as underactuation, robustness, limited power, accuracy, overshoot, etc.). Therefore, a control scheme for Vertical Take Off and Landing (VTOL) multi-rotor Unmanned Aerial Vehicle (UAV) is designed, applying the Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) technique. As reference model based technique, the control specifications are readily met by fixing a desired dynamic model, which is a major advantage of the technique. Moreover, a port −controlled Hamiltonian representation is exploited in order to point out the physical properties of the system such as its internal energy. This latter is exploited, as a fitness function for an optimization algorithm, in order to decrease the consumed energy especially at the take-off step and allows the tuning of the controller parameters. The numerical simulations have shown satisfactory results that support the claims using nominal system model or disturbed model. The designed controller has been implemented on a real vehicle for which one demonstrates, in an indoor area manipulation, the effectiveness of the proposed control strategy.

Yasmina Bestaoui

Papers

UAV Path Planning for Structure Inspection in Windy Environments

3-D Trajectory Planning of Aerial Vehicles Using RRT*

Quadrotor-UAV optimal coverage path planning in cluttered environment with a limited onboard energy

Nonlinear internal model control applied to VTOL multi-rotors UAV

Energy Based 3D Autopilot for VTOL UAV Under Guidance & Navigation Constraints