Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

Tan, Li; Shi, Jiaqi; Wang, Haoyu; Zhang, Hongtao

doi:10.1017/s0263574722001680

Cited by 3 publications

(2 citation statements)

References 19 publications

(19 reference statements)

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“…ABC algorithm was proposed in the early 21st century [1][2]. To solve the path planning problem, the powerful global search capability has attracted many scholars [3][4]. K R, Hong Combining the directed solution update method of the Rao algorithm with the traditional ABC algorithm for minimum dose path planning in complex radiological environments [5].…”

Section: Introductionmentioning

confidence: 99%

Improved artificial bee colony algorithm to solve mobile robot path planning

li,

liu,

Xiao

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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Swarm intelligence algorithms have a large number of applications in solving robot path planning problems. The traditional artificial bee colony (ABC) algorithm has strong global ability, large randomness, however there are problems such as the more nodes, the higher the path complexity. In this study, node movement artificial bee colony algorithm (NMABC) is proposed overcome the disadvantages of the traditional ABC in solving robot path planning problems. The NMABC algorithm contains two novel mechanisms which are the improved initial path generation way and the neighborhood search method of the iterative path of the employed bee. The experimental results show that the NMABC has good global search capability and fast iterative convergence, and it can find the optimal path quickly.

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Section: Introductionmentioning

confidence: 99%

Improved artificial bee colony algorithm to solve mobile robot path planning

li,

liu,

Xiao

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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show abstract

“…To optimize the UAV flight route, heuristic algorithms, which are widely used in UAV path planning [10][11][12][13], are considered. Among those heuristic algorithms [14][15][16][17][18], the artificial bee colony (ABC) algorithm is characterized by simple parameter settings and a strong global search ability, making it particularly suitable for UAV path planning [19][20][21][22][23]. However, the ABC algorithm has certain drawbacks, such as susceptibility to local optima, and extremely simple scout bee activation mechanism.…”

Section: Introductionmentioning

confidence: 99%

A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

Chen,

Liang,

Meng

2023

Remote Sensing

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To obtain more building surface information with fewer images, an unmanned aerial vehicle (UAV) path planning method utilizing an opposition-based learning artificial bee colony (OABC) algorithm is proposed. To evaluate the obtained information, a target information entropy ratio model based on observation angles is proposed, considering the observation angle constraints under two conditions: whether there is an obstacle around the target or not. To efficiently find the optimal observation angles, half of the population that is lower-quality generates bit points through opposition-based learning. The algorithm searches for better individuals near the bit points when generating new solutions. Furthermore, to prevent individuals from observing targets repeatedly from similar angles, the concept of individual abandonment probability is proposed. The algorithm can adaptively abandon similar solutions based on the relative position between the individual and the population. To verify the effectiveness of the proposed method, information acquisition experiments were conducted on real residential buildings, and the results of 3D reconstruction were analyzed. The experiment results show that while model accuracy is comparable to that of the comparison method, the number of images obtained is reduced to one-fourth of the comparison method. The operation time is significantly reduced, and 3D reconstruction efficiency is remarkably improved.

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A Review of Swarm Robotics in a NutShell

Shahzad

Saeed

Akhtar³

et al. 2023

Drones

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A swarm of robots is the coordination of multiple robots that can perform a collective task and solve a problem more efficiently than a single robot. Over the last decade, this area of research has received significant interest from scientists due to its large field of applications in military or civil, including area exploration, target search and rescue, security and surveillance, agriculture, air defense, area coverage and real-time monitoring, providing wireless services, and delivery of goods. This research domain of collective behaviour draws inspiration from self-organizing systems in nature, such as honey bees, fish schools, social insects, bird flocks, and other social animals. By replicating the same set of interaction rules observed in these natural swarm systems, robot swarms can be created. The deployment of robot swarm or group of intelligent robots in a real-world scenario that can collectively perform a task or solve a problem is still a substantial research challenge. Swarm robots are differentiated from multi-agent robots by specific qualifying criteria, including the presence of at least three agents and the sharing of relative information such as altitude, position, and velocity among all agents. Each agent should be intelligent and follow the same set of interaction rules over the whole network. Also, the system’s stability should not be affected by leaving or disconnecting an agent from a swarm. This survey illustrates swarm systems’ basics and draws some projections from its history to its future. It discusses the important features of swarm robots, simulators, real-world applications, and future ideas.

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Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

Cited by 3 publications

References 19 publications

Improved artificial bee colony algorithm to solve mobile robot path planning

Improved artificial bee colony algorithm to solve mobile robot path planning

A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm

A Review of Swarm Robotics in a NutShell

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