An improved heuristic mechanism ant colony optimization algorithm for solving path planning

Liu, Chao; Wu, Lei; Xiao, Wensheng; Li, Guangxin; Xu, Dengpan; Guo, Jingjing; Li, Wentao

doi:10.1016/j.knosys.2023.110540

Cited by 34 publications

(6 citation statements)

References 52 publications

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“…Akka and Khaber [ 89 ] proposed an improved ACO algorithm using a new pheromone evaporation rate to improve the convergence speed and enlarge the search space to avoid local optimal solutions. Moreover, Liu et al [ 90 ] proposed a new, improved heuristic mechanism ant colony algorithm (IHMACO) by improving four search mechanisms of the ant colony algorithm. The search efficiency and ability were further improved.…”

Section: Path Planning Methods and Resultsmentioning

confidence: 99%

A survey on puncture models and path planning algorithms of bevel-tipped flexible needles

Huang,

Yu,

Zhang

2024

Heliyon

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Section: Path Planning Methods and Resultsmentioning

confidence: 99%

A survey on puncture models and path planning algorithms of bevel-tipped flexible needles

Huang,

Yu,

Zhang

2024

Heliyon

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“…Their experimental findings underscore substantial enhancements in both the efficiency and quality of path planning, marking a pivotal advancement in AUV navigation by significantly optimizing for safety, smoothness, and expedited planning capabilities. Chao Liu et al [30] observed that the traditional ACO algorithm suffers from problems such as inefficient search and easy stagnation. Therefore, they proposed a new variant of ACO called Improved Heuristic Mechanism ACO (IHMACO).…”

Section: Related Workmentioning

confidence: 99%

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Meng,

Fang

2024

Electronics

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Aiming at the problem of difficult obstacle avoidance for unmanned ground vehicles (UGVs) in complex dynamic environments, an improved A*-APF algorithm (BA*-MAPF algorithm) is proposed in this paper. Addressing the A* algorithm’s challenges of lengthy paths, excess nodes, and lack of smoothness, the BA*-MAPF algorithm integrates a bidirectional search strategy, applies interpolation to remove redundant nodes, and uses cubic B-spline curves for path smoothing. To rectify the traditional APF algorithm’s issues with local optimization and ineffective dynamic obstacle avoidance, the BA*-MAPF algorithm revises the gravitational field function by incorporating a distance factor, and fine-tunes the repulsive field function to vary with distance. This adjustment ensures a reduction in gravitational force as distance increases and moderates the repulsive force near obstacles, facilitating more effective local path planning and dynamic obstacle navigation. Through our experimental analysis, the BA*-MAPF algorithm has been validated to significantly outperform existing methods in achieving optimal path planning and dynamic obstacle avoidance, thereby markedly boosting path planning efficiency in varied scenarios.

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“…Consequently, many researchers have utilized these metaheuristic global optimization algorithms to address multi-objective path planning. For various environments of mobile robots and autonomous systems, researchers often utilize ant colony optimization algorithms, genetic algorithms, or a combination of both [30][31][32]. These algorithms are commonly integrated with other algorithms to optimize global path planning for a single objective.…”

Section: Related Workmentioning

confidence: 99%

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Zhao,

Zhang,

Shao

et al. 2023

Electronics

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Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction.

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An improved heuristic mechanism ant colony optimization algorithm for solving path planning

Cited by 34 publications

References 52 publications

A survey on puncture models and path planning algorithms of bevel-tipped flexible needles

A survey on puncture models and path planning algorithms of bevel-tipped flexible needles

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

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