Heterogeneous-ants-based path planner for global path planning of mobile robot applications

Lee, Joonwoo

doi:10.1007/s12555-016-0443-6

Cited by 42 publications

(18 citation statements)

References 24 publications

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“…In some complex environments, the algorithm will stagnate if the ants encounter the special obstacles (Lee, 2017), see Figure 3 for an example. To deal with this problem, the early death strategy is usually utilized in literature, i.e.…”

Section: Improved Aco Algorithmmentioning

confidence: 99%

Path planning for coal mine robot via improved ant colony optimization algorithm

Song

Miao

2021

Systems Science & Control Engineering

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This paper is concerned with the path planning of the coal mine robot. A new workspace model is presented to describe the complex coal mine environment. Thus, the cost of a path is composed of not only the distance of the path but also some hybrid costs that can be linked to the criteria of path optimization. To overcome the drawbacks of conventional ant colony optimization (ACO) algorithm, an improved ACO algorithm is developed to tackle the issues of path planning of coal mine robot based on the new workspace model. Some simulation experiments are carried out on the path planning of coal mine robot, and the validity and superiority of the new approach can be confirmed by the simulation results.

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Section: Improved Aco Algorithmmentioning

confidence: 99%

Path planning for coal mine robot via improved ant colony optimization algorithm

Song

Miao

2021

Systems Science & Control Engineering

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“…Calculate P i according to Equation (14); 13 Update V i according to Equation (11); 14 Update X i with quantum-behavior according to Equation (10) X g is output as the global optimal of the fitness function…”

Section: Procedures Of Aoqpiomentioning

confidence: 99%

“…In recent decades, inspired by the organized behavior of natural biological groups, numerous swarm-intelligence optimization algorithms have been proposed to be applied to UAV path planning problem [11,12]. Notable examples include ant colony optimization algorithm (ACO) [13], particle swarm optimization algorithm (PSO) [14], fruit fly optimization algorithm (FOA) [15], and pigeon-inspired optimization algorithm (PIO) [16]. Various merits, including simple structure, general problem adaptability, and rapid search rate, make swam-intelligence optimization algorithms a promising tool for solving UAV path planning problems, especially under varied and complex environments.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Xia

Zhang

2018

Algorithms

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Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

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“…Compared with other heuristics, the ant colony algorithm is characterized by distributed computing, pheromone positive feedback, and strong robustness. As a result, ant colony optimization has been widely used in Recommender systems [21], Feature selection [22], machine layout problem [23], path planning problem [24], and other fields, and has obtained remarkable results. Ant colony algorithms have become a common method for solving robot path planning problems, and therefore, our research group is studying the use of ant colony algorithms to solve robot path planning problems.…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

2020

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To solve the problem of falling into local optimum and poor convergence speed of traditional ant colony algorithm, this paper proposes a High-frequency path mining-based Reward and Punishment mechanism for multi-colony Ant Colony Optimization (HRPACO). Firstly, the pheromone concentration on the path of effective strong association is rewarded adaptively according to the lift of association rules to accelerate the convergence speed. Secondly, the pheromone concentration on the path of minimum spanning tree is punished adaptively according to the support of association rules to improve the diversity of the colony. The interaction of reward and punishment mechanism can effectively balance the diversity and convergence. Finally, a self-evolutionary mechanism based on Gaussian filter is proposed to adaptively adjust the pheromone concentration by dynamic smoothing of the pheromone matrix, so as to help the colony jump out of the local optimum. The TSP is used to verify the performance of the algorithm. The simulation results show that the proposed algorithm can effectively accelerate the convergence speed and improve the accuracy of solution, especially for large-scale problems. Meanwhile, path planning is used to verify the feasibility of the proposed algorithm. The simulation results show that the algorithm can find an effective and better path even in the environment of complex obstacles.

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Heterogeneous-ants-based path planner for global path planning of mobile robot applications

Cited by 42 publications

References 24 publications

Path planning for coal mine robot via improved ant colony optimization algorithm

Path planning for coal mine robot via improved ant colony optimization algorithm

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

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