An improved beetle antennae search path planning algorithm for vehicles

Qing, Liang; Zhou, Huike; Yin, Yongsheng; Xiong, Wei

doi:10.1371/journal.pone.0274646

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Experimental simulations with UAVs not only validated the effectiveness of the OABAS algorithm but also confirmed its superiority in comparison to other bio-heuristic algorithms. Deng et al proposed a BAS-optimized path tracking and collision avoidance guidance method for unmanned sailing ships (Liang et al 2022). This method adeptly determines the optimal heading angle to minimize the total cost function, thereby efficiently achieving path tracking and collision avoidance in marine navigation.…”

Section: Path Planning and Traffic Systemsmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.

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Section: Path Planning and Traffic Systemsmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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“…In order to realize autonomous intelligent flight of UAV in low altitude complex environment, it is particularly important to research the perception of flight environment and the avoidance of obstacles [ 5 ]. Different airborne sensors have different environmental perception degrees and effects [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Autonomous aerial obstacle avoidance using LiDAR sensor fusion

et al. 2023

Self Cite

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The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.

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“…All of these methods use Eppstein's k-best algorithm to find the optimal UAV path. The third group of optimization techniques, which are also called population-based evolutionary algorithms, are now widely accepted and used to solve the UAV path-planning problem, including particle swarm optimization (PSO) [16,17], genetic algorithm (GA) [18,19], firefly algorithm (FA) [20,21], beetle antennae search (BAS) [22,23], and related improved methods [24,25]. When using graph-searchingbased algorithms, such as the Dijkstra algorithm, their expansion in every direction and blind recalculation of each direction is computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

A Ground-Risk-Map-Based Path-Planning Algorithm for UAVs in an Urban Environment with Beetle Swarm Optimization

Zhang,

Liu,

Gao

et al. 2023

Applied Sciences

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This paper presents a path-planning strategy for unmanned aerial vehicles (UAVs) in urban environments with a ground risk map. The aim is to generate a UAV path that minimizes the ground risk as well as the flying cost, enforcing safety and efficiency over inhabited areas. A quantitative model is proposed to evaluate the ground risk, which is then used as a risk constraint for UAV path optimization. Subsequently, beetle swarm optimization (BSO) is proposed based on a beetle antennae search (BAS) that considers turning angles and path length. In this proposed BSO, an adaptive step size for every beetle and a random proportionality coefficient mechanism are designed to improve the deficiencies of the local optimum and slow convergence. Furthermore, a global optimum attraction operator is established to share the social information in a swarm to lead to the global best position in the search space. Experiments were performed and compared with particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA), and BAS. This case study shows that the proposed BSO works well with different swarm sizes, beetle dimensions, and iterations. It outperforms the aforementioned methods not only in terms of efficiency but also in terms of accuracy. The simulation results confirm the suitability of the proposed BSO approach.

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An improved beetle antennae search path planning algorithm for vehicles

Cited by 3 publications

References 26 publications

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Autonomous aerial obstacle avoidance using LiDAR sensor fusion

A Ground-Risk-Map-Based Path-Planning Algorithm for UAVs in an Urban Environment with Beetle Swarm Optimization

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