Complex Environment Path Planning for Unmanned Aerial Vehicles

Zhang, Jing; Ji-wu, LI; Yang, Hongwei; Feng, Xia‐Ting; Sun, Geng

doi:10.3390/s21155250

Cited by 13 publications

(12 citation statements)

References 47 publications

(67 reference statements)

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“…Robot technology also originated in the United States. At present, the performance of robots studied in the United States is still at the forefront of the industry [7]. Many soldiers in the United States are physically disabled and unable to walk normally due to their participation in the war, which seriously affects their daily life.…”

Section: Literature Reviewmentioning

confidence: 99%

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Zhao

2022

Journal of Sensors

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In this paper, warehouse logistics robot as the research object, starting from the reality of modern e-commerce logistics industry, proposed a warehousing logistics mobile robot path planning method. Ant colony algorithm is used to plan the forward path of mobile robot in static and dynamic environment of warehouse logistics. The results show that the elite-based strategy improves the global search capability of ACA and eliminates redundant nodes in the path, and the path length of the obtained plan is significantly better than that of traditional ACA, which is reduced by 3.46% and 5.90%, respectively, indicating that the elite-based strategy and the central-point-based smoothing method play their roles. The path length of the robot’s final operation is larger than that of the global path planning, which increases by 1, accounting for 6.67% of the original path length. Therefore, the storage and logistics mobile robot based on ACA-E algorithm has short driving distance and superior obstacle avoidance ability.

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Section: Literature Reviewmentioning

confidence: 99%

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Zhao

2022

Journal of Sensors

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“…Huang and Zhang [6,7] improved the defects of genetic algorithm and RRT algorithm respectively, and proposed an improved UAV path planning algorithm. Zhang [8] established an objective function based on the minimum flight time, battery energy consumption, and risk of drones, and studied the drone navigation planning between two freight warehouses in urban areas. Yang [9] studied the path planning scheme of mobile robot considering path obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

Urban Area End Logistics Drones Distribution Route Planning

Tan¹

2023

IJEH

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With the development of logistics technology, the application of drones in the last mile of logistics distribution has also become a hot topic. Due to the complexity and particularity of the flight environment in urban areas, UAVs need to comprehensively consider safety performance and flight energy consumption when conducting terminal logistics distribution. Based on this, this paper constructs a mathematical model with the goal of shortening flight paths, reducing energy consumption, and improving flight safety, and completes environmental modeling using grid method and genetic algorithm to solve the model. At the same time, the battery energy consumption of the UAV is calculated using the energy consumption segmentation mode. The experimental results of a numerical example verify the effectiveness of the model and algorithm, and also prove that considering segmented energy consumption can more accurately calculate the battery energy consumption of unmanned aerial vehicles.

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“…Zhang et al proposed a branch selection rapid exploration random tree (BS-RRT) algorithm to solve the global path planning problem in the narrow channel environment of UAVs. However, these two algorithms are aimed at aerial unmanned equipment, which is not the same as the background of this study [ 26 ]. Yu et al combined the improved Grey Wolf Optimization algorithm with the

Light algorithm and proposed a multi-target path planning algorithm for an unmanned cruise ship in an unknown obstacle environment [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method

2022

Sensors

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Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm.

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Complex Environment Path Planning for Unmanned Aerial Vehicles

Cited by 13 publications

References 47 publications

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

Urban Area End Logistics Drones Distribution Route Planning

Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method

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