Multi-obstacle Path Planning of UAV Based on Improved Ant Colony System Algorithm

Ling, Fuyuan; Chen, Jinchao; Du, Chenglie

doi:10.1109/itoec49072.2020.9141864

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Meanwhile, a search strategy based on the ant colony system (ACS) is proposed to find a search sequence with the shortest flying distance. e path length obtained by IGA-ACS is shorter, and the running time is very short [26]. Yang and Zhang examine the maneuver trajectory generation methods for UAVs based on energy optimization and design appropriate maneuver commands in the UAV flight envelope to achieve optimal energy performance [27].…”

Section: Related Workmentioning

confidence: 99%

Volcanic Ash Region Path Planning Based on Improved A-Star Algorithm

Zhang

Meng

et al. 2022

Journal of Advanced Transportation

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In civil aviation flight path planning, in order to effectively reduce the safety threat caused by the volcanic ash area to the civil aviation flight, factors such as the speed and acceleration of the aircraft in the volcanic ash area must be considered. In this paper, we propose an improved A-star algorithm by adopting the concept of potential collision set and using the velocity vector idea of optimal reciprocal collision avoidance (ORCA). The improved A-star algorithm selects the optimal speed range of the aircraft from the limited elements, obtains the speed and acceleration set of the aircraft in the volcanic ash area, calculates the flight path through the displacement increment, continuously refreshes the global starting point to the target point, and finally obtains the final path planning results by calculating the total cost value. The improved A-star algorithm is used to plan two flight paths from Madrid to Cairo and Algiers to Rome in volcanic ash areas. The verification results show that the improved A-star algorithm optimizes the flight path planning in the volcanic ash area and has the advantages of less search nodes, a small search range, and short computing time.

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Section: Related Workmentioning

confidence: 99%

Volcanic Ash Region Path Planning Based on Improved A-Star Algorithm

Zhang

Meng

et al. 2022

Journal of Advanced Transportation

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A Survey of Indoor UAV Obstacle Avoidance Research

Xiong

Yan

et al. 2023

IEEE Access

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UAV obstacle avoidance technology is one of the key factors to realize UAV autonomous flight, efficient and accurate obstacle avoidance is significant to complete the UAV autonomous flight task. In contrast, the dynamic, real-time and uncertainty of the environment in which the UAV is located makes the problem very tricky, especially in the indoor environment. At present, a large number of scholars have shown strong interest in the indoor UAV obstacle avoidance problem. With the rapid development of computer technology and hardware devices, many intelligent algorithms have been proposed to solve the obstacle avoidance problem. However, the research on indoor UAV obstacle avoidance technology is not comprehensive enough, and there is a lack of summarization of the research results in recent years. This paper introduces the sensor modules commonly used for indoor UAV environment sensing, related obstacle avoidance methods based on sensory detection. Classifies and composes the commonly used UAV path planning obstacle avoidance algorithms, and gives several representative UAV flight control research methods. This paper summarizes the advantages and disadvantages of different perception modules and detection methods applied to UAV obstacle avoidance tasks, and compares various current path planning methods. Finally, the critical difficulties and challenges faced in the field of indoor UAV obstacle avoidance are discussed, and future research in the field of UAV obstacle avoidance has prospected.

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