Three-Dimensional Autonomous Obstacle Avoidance Algorithm for UAV Based on Circular Arc Trajectory

Jian-dong, Guo; Liang, Chenyu; Wang, Kang; Sang, Biao; Wu, Yulin

doi:10.1155/2021/8819618

Cited by 14 publications

(5 citation statements)

References 26 publications

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“…Aiming at the generation and optimization of the search path, currently, the general research goal is to maximize the efficiency of task execution by effectively modeling the obstacle area [19,20], and then, the artificial potential field (APF) method [9], the improved RRT * algorithm [21][22][23][24], and heuristic algorithms have been employed to generate an optimal path while solving spatial conflicts. The Circular Arc Trajectory method was used to model the irregular obstacle area, which could simplify the computational complexity of the obstacle avoidance problem [19]. The improved APF algorithm and a path optimization method for multiobstacle avoidance were proposed to solve the problem of path of UAV swarm falling into deadlock [20].…”

Section: Related Workmentioning

confidence: 99%

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Cheng

et al. 2022

International Journal of Aerospace Engineering

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Aiming to address the problem of unknown dynamic target trajectory prediction and search path optimization in unmanned aerial vehicle (UAV) swarm path planning, this paper proposes a target search algorithm based on a modified target probability map (TPM). First, using the TPM, the proposed algorithm generates a high-probability distribution region of a target with directionality to fit the target trajectory and realizes the trajectory prediction of an unknown dynamic target. Then, the distributed ant colony (ACO) algorithm and the artificial potential field (APF) algorithm are combined to generate and optimize the UAV swarm search result and return path with the goal of maximizing task execution efficiency. Finally, the Monte Carlo simulation method is used to analyze the effectiveness of the proposed algorithm, and the results are evaluated from five aspects, including the number of targets captured. The simulation results show that under the condition of an unknown dynamic target trajectory, the average target captured rate and average unknown region search rate of the MTPM method were higher than that of the traditional TPM method, and the performance was improved by 14.6% and 10.7%, respectively.

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Section: Related Workmentioning

confidence: 99%

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Cheng

et al. 2022

International Journal of Aerospace Engineering

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“…The algorithm of the obstacle avoidance system designed is adapted from the research [16] carried out by making several modifications to match the system designed.…”

Section: Obstacle Avoidance System Designmentioning

confidence: 99%

Autonomous Quadcopter Control System Design Using LQG Controller to Perform Obstacle Avoidance

Darwito¹,

Alfathrah²,

Nugroho³

et al. 2022

Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Mechanical and E

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This research presents the implementation of the Linear Quadratic Gaussian (LQG) Controller and LiDAR sensors on the quadcopter used to avoid obstacles. The LQG controller is a combination of a Linear Quadratic Regulator (LQR) with a Kalman Filter as a State Estimator. The results of the estimate by Kalman Filter are compared against the actual value of the measurement by reviewing the Mean Square Error value. Linear position and angular position are variables that are controlled in the LQG control system to perform obstacle avoidance. The variations used in this study are the Q and R matrix values in the LQG controller design. Some of the tests that have been carried out include the open loop test, the closed loop test, the quadcopter test without an obstacle and the quadcopter test with an obstacle. The test results show that the quadcopter is able to follow the flight reference path and that the obstacle avoidance algorithm designed produces a quadcopter that is able to avoid obstacles. The test results show that the Q and R matrices on the Gain Regulator and Kalman filters can affect the quadcopter in carrying out avoidance.

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“…However, it is essential to note that many of these approaches primarily cater to rotary-wing UAVs capable of stopping and hovering, rendering them less suitable for fixed-wing UAVs. For fixed-wing aircraft, a range of geometric approaches, such as forming circular arcs or Three-Dimensional (3D) trajectories [11][12][13], velocity modulation in the horizontal plane [14], velocity obstacle concepts [15][16][17][18], differential geometry concepts [19], and the use of collision cones [20,21], have been proposed. While these methods provide some applicability to fixed-wing aircraft, they may suffer from limitations such as high computational demands and a lack of intuitive understanding.…”

Section: Introductionmentioning

confidence: 99%

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

Rauniyar,

Kim

2023

Electronics

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Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel-inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space.

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Three-Dimensional Autonomous Obstacle Avoidance Algorithm for UAV Based on Circular Arc Trajectory

Cited by 14 publications

References 26 publications

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Autonomous Quadcopter Control System Design Using LQG Controller to Perform Obstacle Avoidance

Genetic Fuzzy Inference System-Based Three-Dimensional Resolution Algorithm for Collision Avoidance of Fixed-Wing UAVs

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