3D Multi-UAV Collaboration Based on the Hybrid Algorithm of Artificial Bee Colony and A*

Bai, Xin; Wang, Ping; Wang, Ziye; Zhang, Lan

doi:10.23919/chicc.2019.8866197

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…Bai et al [95] proposed a hybrid algorithm based on Artificial Bee Colony algorithm (ABC) and A*. The algorithm uses the ABC algorithm to complete the preliminary planning, then uses the A* algorithm to plan the specific trajectory points, and finally combines the adaptive time coordination method to obtain the optimal trajectory, which solves the problem of three-dimensional multi-UAV trajectory planning.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

UAV Formation Trajectory Planning Algorithms: A Review

Yang

Xiong

Yan

2023

Drones

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With the continuous development of UAV technology and swarm intelligence technology, the UAV formation cooperative mission has attracted wide attention because of its remarkable function and flexibility to complete complex and changeable tasks, such as search and rescue, resource exploration, reconnaissance and surveillance. The collaborative trajectory planning of UAV formation is a key part of the task execution. This paper attempts to provide a comprehensive review of UAV formation trajectory planning algorithms. Firstly, from the perspective of global planning and local planning, a simple framework of the UAV formation trajectory planning algorithm is proposed, which is the basis of comprehensive classification of different types of algorithms. According to the proposed framework, a classification method of existing UAV formation trajectory planning algorithms is proposed, and then, different types of algorithms are described and analyzed statistically. Finally, the challenges and future research directions of the UAV formation trajectory planning algorithm are summarized and prospected according to the actual requirements. It provides reference information for researchers and workers engaged in the formation flight of UAVs.

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Section: Artificial Bee Colonymentioning

confidence: 99%

UAV Formation Trajectory Planning Algorithms: A Review

Yang

Xiong

Yan

2023

Drones

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“…So far, these methods have been widely used for path planning of different kinds of delivery vehicles. A * algorithm is used to guide UAVs to find a safe path in battle fields [33], and A * algorithm is combined with artificial bee colony to guide the motion of multiple UAVs in a 3D environment [34]. In an underwater environment, the A * algorithm is adopted to guide underwater robots to track chemical plume [35].…”

Section: Heuristic Searching Algorithmsmentioning

confidence: 99%

Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles

Ding

et al. 2021

Mathematical Problems in Engineering

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With the development of e-commerce, the last-mile delivery has become a significant part of customers’ shopping experience. In this paper, an autonomous last-mile delivery method using multiple unmanned ground vehicles is investigated. Being a smart logistics service, it provides a promising solution to reduce the delivery cost, improve efficiency, and avoid the spread of airborne diseases, such as SARS and COVID-19. By using a cooperation strategy with multiple heterogeneous robots, contactless parcel delivery can be carried out within apartment complexes efficiently. In this paper, the last-mile delivery with heterogeneous UGVs is formulated as an optimization problem aimed at minimizing the maximum makespan to complete all tasks. Then, a heuristic algorithm combining the Floyd’s algorithm and PSO algorithm is proposed for task assignment and path planning. This algorithm is further realized in a distributed scheme, with all robots in a swarm working together to obtain the best task schedule. A good solution with an optimized makespan is achieved by considering the constraints of various robots in terms of speed and payload. Simulations and experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.

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“…Figure 13: Comparison between ABC and PRM Bai et al [51] introduced a hybrid between A* and artificial bee colony to realize 3D Unmanned Aerial Vehicles (UAV) path planning. ABC uses ant first to generate different initial path nodes to reduce computational time.…”

Section: Bee Colonymentioning

confidence: 99%

Assessment and Review of the Reactive Mobile Robot Navigation

Yosif¹,

Mahmood²,

Al-Khayyt³

2021

(AREJ)

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Nowadays, the mobile robot can be seen in different fields of engineering and science. The mobile robot can do some tasks that are so difficult or very risky to be performed by a human. Most of the works currently focus on implementing artificially intelligent algorithms and other algorithms that depend on the behaviour of nature. These approaches have been used in mobile robot navigation along uncertain manner. Mobile robot navigation strategies can be divided into two approaches: the classical approach and reactive approach. The classical approach related to static environment, whiles the reactive navigation is based on an unstructured environment. Path planning is one of the most important parts of the navigation system. In this paper, review and assessment of path planning strategies that can concern with the reactive approach are discussed, because it deal with the problem of dynamic environment. Numerous reactive methods have been introduced. Most of these presented works were concerned with simulation and a few of them have shown experimental implementation. Many papers tried to make a combination between two algorithms or more to increase the efficiency. It is concluded that reactive algorithms need more learning phases, complex in design, and require large memory storage.

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3D Multi-UAV Collaboration Based on the Hybrid Algorithm of Artificial Bee Colony and A*

Cited by 10 publications

References 13 publications

UAV Formation Trajectory Planning Algorithms: A Review

UAV Formation Trajectory Planning Algorithms: A Review

Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles

Assessment and Review of the Reactive Mobile Robot Navigation

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