Obstacle avoidance path planning of unmanned submarine vehicle in ocean current environment based on improved firework-ant colony algorithm

Ma, Yiwei; Mao, Zhaoyong; Wang, Tao; Qin, Jian; Ding, Wenjun; Meng, Xiangyao

doi:10.1016/j.compeleceng.2020.106773

Cited by 35 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yan Ma et al [198] worked on an underwater vehicle system for path planning by improving the traditional Ant Colony Algorithm with Fireworks. In the first step, the Lamb vortex creates a 2D environment model with a random distribution of obstacles; in the second step, a mathematical model is established for calculating time, distance, and energy consumption cost.…”

Section: Application To Underwater Vehiclesmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

View full text Add to dashboard Cite

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

show abstract

Section: Application To Underwater Vehiclesmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The study of heuristic algorithm improvement strategies for path planning problems has become popular nowadays. The artificial potential field method is one of the classical algorithms applied to path planning, but suffers from the defects of not being able to reach the target point and easily falling into the local optimum [12]. In response, scholars have taken a series of measures to address the above issues.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Multi-Robot Formation Control and Global Path Optimization

2022

View full text Add to dashboard Cite

For multi-robot cooperative formation and global path planning, we propose to adjust the repulsive field function and insert a dynamic virtual target point to solve the local minima and target unreachability problems of the traditional artificial potential field (APF) method. The convergence speed and global optimization accuracy of ant colony optimization (ACO) are improved by introducing improved state transfer functions with heuristic information of the artificial potential field method and optimizing the pheromone concentration update rules. A hybrid algorithm combining APF and improved ACO is used to calculate an optimal path from the starting point to the end point for the leader robot. A cooperative multi-robot formation control method combining graph theory and consistency algorithm is proposed based on path planning of the leader robot. Taking AGVs in a logistics distribution center as an example, the feasibility of the improved algorithm and its effectiveness in solving the multi-robot path planning problem are verified.

show abstract

“…With the development of algorithms, the ACO algorithm gradually began to combine with other algorithms to achieve the purpose of an optimization algorithm. Ma et al 19 used the improved FA to generate the initial pheromone concentration values to distribute them on the map and then search for a globally optimal path through the ACO algorithm. This algorithm can find the optimal path in the case of fewer iterations.…”

Section: Introductionmentioning

confidence: 99%

A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

Chen

Zhang

et al. 2022

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

To improve the finding path accuracy of the ant colony algorithm and reduce the number of turns, a jump point search improved ant colony optimization hybrid algorithm has been proposed in this article. Firstly, the initial pheromone concentration distribution gets from the jump points has been introduced to guide the algorithm in finding the way, thus accelerating the early iteration speed. The turning cost factor in the heuristic function has been designed to improve the smoothness of the path. Finally, the adaptive reward and punishment factor, and the Max–Min ant system have been introduced to improve the iterative speed and global search ability of the algorithm. Simulation through three maps of different scales is carried out. Furthermore, the results prove that the jump point search improved ant colony optimization hybrid algorithm performs effectively in finding path accuracy and reducing the number of turns.

show abstract

Obstacle avoidance path planning of unmanned submarine vehicle in ocean current environment based on improved firework-ant colony algorithm

Cited by 35 publications

References 9 publications

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

Collaborative Multi-Robot Formation Control and Global Path Optimization

A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

Contact Info

Product

Resources

About