The Comparison of Four UAV Path Planning Algorithms Based on Geometry Search Algorithm

Obaidat

et al. 2020

Int J Communication

SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Obaidat

et al. 2020

Int J Communication

“…The drone's path planning problem for area coverage is to plan the flight path to cover all points in an area at the lowest possible cost [16], therefore, drone's flight path planning methods for fixed target points have reference significance for drone's area coverage path planning. He and Zhao [17] compares the performance on online real-time path planning abilities for four algorithms in different applying situations of drones, and the computational results indicate that the Dijkstra algorithm performs the best, Guan et al [18] proposed a drone's path planning algorithm based on double ant colony, through using genetic algorithm to generate pheromone in the early stage, the convergence of ant colony algorithm was improved. Avellar et al [19] reviewed the civil applications of the drone and presented a comprehensive illustration on the covering problems by drones.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Two random stopping nodes are selected in each area, and then the distance for the drone from the stopping node for taking off to the start point of the scanning path and the distance from the end points of the scanning path to the stopping node for landing is calculated, and Dis is the sum of the two distance (Line 4-10). In every area we find the two stopping nodes corresponding to the shortest distance Dis as the selected stopping nodes (Line 12) to form the goal (Line 14), then calculate the saving value matrix (Line [15][16][17][18][19], and arrange it in descending order (Line 20). Starting from the maximum value, the corresponding two areas are connected until all areas are included, and the initial feasible solution is obtained (Line 21).…”

Section: B Stage 2: Route Planning Of Gvmentioning

confidence: 99%

Two-Layer Path Planning for Multi-Area Coverage by a Cooperated Ground Vehicle and Drone System

Xia¹,

Chen²,

Shi³

et al. 2020

Preprint

A novel two-layer path planning method for a cooperated ground vehicle (GV) and drone system is investigated, where the GV acts as the mobile platform of the drone and is used to conduct multiple area covering tasks collaboratively. The GV takes the drone to visit a set of discrete areas, while the drone takes off from the GV at potential nodes around each area and scans each area for collecting information. The drone can be recharged in the GV during the time when it travels between different areas. The objective is to optimize the drone’s scanning path for all areas’ coverage and the GV’s travel path for visiting all areas. A 0-1 integer programming model is developed to formulate the problem. A two-stage heuristic based on cost saving strategy is designed to quickly construct a feasible solution, then the Adaptive Large Neighborhood Search (ALNS) algorithm is employed to improve the quality of the solution. A simulation experiment based on the parks in Changsha, China, is presented to illustrate the application of the method. Random instances are designed to further test the performance of the proposed algorithm.

“…The predicted GNSS error over the test domain is used to define a cost-map that is applied to the guidance problem. Several options are applicable for this use-case including the well-known A* algorithm [39]. This is an extension of the Djikstras algorithm for finding the least-cost path between designated start and destination grid cells.…”

Section: Gnss Performance Characterizationmentioning

confidence: 99%

GNSS Performance Modelling and Augmentation for Urban Air Mobility

Bijjahalli

Sabatini

Gardi

2019

Sensors

One of the primary challenges facing Urban Air Mobility (UAM) and the safe integration of Unmanned Aircraft Systems (UAS) in the urban airspace is the availability of robust, reliable navigation and Sense-and-Avoid (SAA) systems. Global Navigation Satellite Systems (GNSS) are typically the primary source of positioning for most air and ground vehicles and for a growing number of UAS applications; however, their performance is frequently inadequate in such challenging environments. This paper performs a comprehensive analysis of GNSS performance for UAS operations with a focus on failure modes in urban environments. Based on the analysis, a guidance strategy is developed which accounts for the influence of urban structures on GNSS performance. A simulation case study representative of UAS operations in urban environments is conducted to assess the validity of the proposed approach. Results show improved accuracy (approximately 25%) and availability when compared against a conventional minimum-distance guidance strategy.