ACO+PSO+A*: A bi-layer hybrid algorithm for multi-task path planning of an AUV

Sui, Fuli; Tang, Xiaoke; Dong, Zihao; Gan, Xingjia; Luo, Peng; Sun, Jun

doi:10.1016/j.cie.2022.108905

Cited by 35 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The force 𝐹𝐹 that determines the direction of motion of a robot can be derived by 𝐹𝐹 𝑎𝑎𝑎𝑎𝑎𝑎 and 𝐹𝐹 𝑟𝑟𝑟𝑟𝑟𝑟 as in the formula (7).…”

Section: Traditional Apf Algorithmmentioning

confidence: 99%

“…Travel distance and energy expenditure are also used to determine the updated pheromone. Sui Fuli, et al [7] proposed a bi-level multi-objective path planning model, which aiming at minimum path length and dangerous distance to solve the multitask path planning problem of underwater vehicle (AUV). The algorithm uses ant colony optimization algorithm to generate the task sequence of the outer-layer model, while particle swarm optimization algorithm (PSO) is used to generate the waypoints between adjacent tasks, at the same time A * algorithm is used to generate the collision-free path of the lower model based on the inner path point.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective path planning for mobile robot based on ACO -APF fusion algorithm

Zhao,

Qiao,

Feng

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

Path planning of mobile robot is an important research direction of mobile robot, there are some defects in the path planned by the traditional Ant Colony Optimization (ACO) algorithm, many turning points appear in the planned path, some points in the planned path are very closed to the obstacles, all of these increase the probability that the mobile robot will hit an obstacle. Although the path planned by the Artificial Potential Field (APF) algorithm is relatively smooth, it often has redundant segments, and at the same time, it is not necessarily the optimal path. In order to solve these problems, ACO -APF fusion algorithm is proposed in this paper. The path planned by the ACO -APF fusion algorithm can keep a safe distance from the obstacles and have fewer turning points. To verify the performance of the fusion algorithm, the path smoothing performance and the select performance are tested. The ACO -APF fusion algorithm can also be used for multi-objective point path planning. In the simulation test, the grid map of the hospital scene is created, by inputting the starting point and different target points in the algorithm, a path containing four target points is planned. Simulation results show that ACO -APF fusion algorithm can plan out multi-objective point path according to user requirements.

show abstract

“…The force 𝐹𝐹 that determines the direction of motion of a robot can be derived by 𝐹𝐹 𝑎𝑎𝑎𝑎𝑎𝑎 and 𝐹𝐹 𝑟𝑟𝑟𝑟𝑟𝑟 as in the formula (7).…”

Section: Traditional Apf Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective path planning for mobile robot based on ACO -APF fusion algorithm

Zhao,

Qiao,

Feng

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

show abstract

“…Pal et al [ 17 ] suggested an ant colony optimization algorithm that incorporates a fuzzy logic-based contingency ranking index for the transmission and contact lines of a multi-region grid; furthermore, an enhanced ant colony algorithm was employed to reduce transmission active and reactive power losses during contingencies, thus augmenting power flow through other transmission lines. A two-tier multi-objective path planning model was proposed by Sui et al [ 18 ] with the objective of minimizing path length and hazard distance. The algorithm utilizes an ant colony optimization algorithm to create a series of tasks for the upper model in the outer layer and then creates a two-layer hybrid algorithm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Song,

Pu,

2024

Sensors

View full text Add to dashboard Cite

For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displacement sensor probe to optimize the alignment of its measurement velocity and accuracy. This manuscript proposes a 3D surface laser scanning path planning technique that utilizes adaptive ant colony optimization with sub-population and fuzzy logic (SFACO), which involves the consideration of the measurement point layout, probe attitude, and path planning. Firstly, this study is based on a four-coordinate measuring machine paired with a point laser displacement sensor probe. The laser scanning four-coordinate measuring instrument is used to establish a coordinate system, and the relationship between them is transformed. The readings of each axis of the object being measured under the normal measuring attitude are then reversed through the coordinate system transformation, thus resulting in the optimal measuring attitude. The nominal distance matrix, which demonstrates the significance of the optimal measuring attitude, is then created based on the readings of all the points to be measured. Subsequently, a fuzzy ACO algorithm that integrates multiple swarm adaptive and dynamic domain structures is suggested to enhance the algorithm’s performance by refining and utilizing multiple swarm adaptive and fuzzy operators. The efficacy of the algorithm is verified through experiments with 13 popular TSP benchmark datasets, thereby demonstrating the complexity of the SFACO approach. Ultimately, the path planning problem of surface 3D laser scanning measurement is addressed by employing the proposed SFACO algorithm in conjunction with a nominal distance matrix.

show abstract

“…Zhu [36] introduced a hybrid ant colony optimization algorithm, which uses distance, demand, and time as heuristic factors to improve its applicability in various domains. Regarding the application of fused ant colony algorithms, Sui et al [37] have proposed an ACO + PSO + A* algorithm model for multi-task path planning, which produces optimal collision-free path planning with shorter generation length and higher safety. Lyridis [38] has designed an improvement of ACO that incorporates fuzzy logic to address the multi-objective problem of USV path planning and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

Geo-Economic Analysis Based on an Improved Ant Colony Optimization

et al. 2023

View full text Add to dashboard Cite

This paper addresses the optimal path selection problem for economic corridors, which is a significant issue in the field of geo-economics. The paper has utilized the spatiotemporal characteristics of geo-economics and identified the development needs in this field to propose an improved ant colony optimization (ACO) strategy. The proposed strategy focuses on enhancing the heuristic function, functional area setting, and pheromone updating strategy. The heuristic factors and transfer probabilities have been improved to couple the path nature, which were based on an analysis of the factors that influence geo-economics. This improvement enhances the applicability of the ACO to path selection problems in macrospace. Additionally, the paper has differentiated the priority of path nodes by setting functional areas, which adds directionality to path selection. The improved ACO has been applied to analyze the optimal path in macroscopic economic space. The experimental validation was conducted in the Indo-Pacific region and economic corridors in China within this region, and corresponding potential geo-economic hubs were analyzed. The experimental results were validated using the Mann−Whitney U test and an evaluation method based on path effectiveness. The feasibility and objectivity of the proposed method were verified. This research provides a valuable exploration of the problem of path selection in macrospace and time and provides decision aid for the construction and adjustment development of a country’s geo-economic relations in a given region. It is a technical reference for establishing sustainable development strategies and national and regional economic planning. Overall, this work contributes significantly to the field of geo-economics and demonstrates the effectiveness of the proposed method through experimental validation.

show abstract

ACO+PSO+A*: A bi-layer hybrid algorithm for multi-task path planning of an AUV

Cited by 35 publications

References 22 publications

Multi-objective path planning for mobile robot based on ACO -APF fusion algorithm

Multi-objective path planning for mobile robot based on ACO -APF fusion algorithm

Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Geo-Economic Analysis Based on an Improved Ant Colony Optimization

Contact Info

Product

Resources

About