The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO.

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

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

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

Unmanned aerial vehicles applications in future smart cities

https://www.tpbin.com/Uploads/Subjects/e09500b9-2256-4132-b3be-f6cb0b600a0d.pdf

Coverage path planning for UAVs based on enhanced exact cellular decomposition method

This paper presents the autonomous control for UAV. The autonomous control concept and Autonomous Control Level (ACL) metrics that can measure autonomy of UAVs are introduced. Compared with manned aircraft control task in battlefield, the functions of autonomous UAV system are organized. According to the laws of Increasing Precision with Decreasing Intelligent (IPDI), the architecture of autonomous control for UAV is given. The architecture can be divided into three levels: Execution Level, Coordination Level and Organization Level. The constraint conditions and realizations of Coordination Level and Organization Level are studied comprehensively. The key hardware and software technologies for multi-tasks are modularized depending on the requirements of mission. The software technologies are distributed to stages of flying particularly.

A Survey of Autonomous Control for UAV

This paper proposes an improved method of exact cellular decomposition to plan the coverage path in polygon area. Firstly, the problem of Coverage Path Planning (CPP) in the convex polygon area is transformed to the width calculation of the convex polygon, and the strategy of flying along the vertical direction of width is presented to reach the least number of turns. Secondly, a convex decomposition algorithm of minimum width sum based on the greedy recursive method is presented to decompose the concave area into some convex subregions. Finally, a subregion connection algorithm based on minimum traversal of undirected graph is proposed to connect the coverage paths of the subregions. The simulation shows that the proposed method is feasible and effective.

Research on the coverage path planning of UAVs for polygon areas

A level measurement system based on FMCW radar is developed. The structure of the measurement system is introduced. The methods of digital signal processing for the system are investigated. FFT algorithm is employed to process frequency difference in low-accuracy situation. When the signal-to-noise ratio is high, the high accuracy measurement is obtained by the AR spectral estimation. An autocorrelation filter is applied to the measurement system to reduce the disturbances. The resolution of the system which employs the methods of signal processing reaches 1 mm.

Digital Signal Processing for A Level Measurement System Based on FMCW Radar

This paper investigates the search probability and camera footprint of region coverage for UAVs Two kinds of search probability by using the UAV camera are proposed Moreover, the relationship between the UAV altitude and the camera footprint is also studied in this paper Some Formulars concerned with the UAV camera footprint are developed in two cases: flat flying and roll motion, and yaw motion The formulars can be used to obtain the length and width of the camera footprint and the coordinate transformation between the UAV centroid and the footprint center respectively Finally, the front-mounted angle of camera is discussed in this paper to ensure the region coverage more effective

Research on search probability and camera footprint of region coverage for UAVs

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