Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment

Xiong, Ni; Zhou, Xinzhi; Yang, Xiuqing; Xiang, Yong; Ma, Junyong

doi:10.3389/fnbot.2021.642733

Cited by 22 publications

(12 citation statements)

References 24 publications

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“…Where, τ ij is the pheromone content from node i to node j , η ij is the heuristic function, d ij is the Euclidean distance from node i to node j , α is the pheromone heuristic factor, β is the expected heuristic factor, and allowed k is the set of optional nodes in the next step (Xiong et al, 2021 ).…”

Section: Ant Colony Optimization Algorithmmentioning

confidence: 99%

Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm

Wang

Yang

et al. 2022

Front. Neurorobot.

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Aiming at the problems of slow convergence and easy fall into local optimal solution of the classic ant colony algorithm in path planning, an improved ant colony algorithm is proposed. Firstly, the Floyd algorithm is introduced to generate the guiding path, and increase the pheromone content on the guiding path. Through the difference in initial pheromone, the ant colony is guided to quickly find the target node. Secondly, the fallback strategy is applied to reduce the number of ants who die due to falling into the trap to increase the probability of ants finding the target node. Thirdly, the gravity concept in the artificial potential field method and the concept of distance from the optional node to the target node are introduced to improve the heuristic function to make up for the fallback strategy on the convergence speed of the algorithm. Fourthly, a multi-objective optimization function is proposed, which comprehensively considers the three indexes of path length, security, and energy consumption and combines the dynamic optimization idea to optimize the pheromone update method, to avoid the algorithm falling into the local optimal solution and improve the comprehensive quality of the path. Finally, according to the connectivity principle and quadratic B-spline curve optimization method, the path nodes are optimized to shorten the path length effectively.

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Section: Ant Colony Optimization Algorithmmentioning

confidence: 99%

Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm

Wang

Yang

et al. 2022

Front. Neurorobot.

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“…The process stage of the ant colony optimization method starts when the ant moves from the anthill to the food source, the ant will pass through several existing paths to get to the food source [11]. When passing through, the ant's path will leave a pheromone substance as a sign that the ant is passing through the path.…”

Section: Figure 2 Ant Colony Optimization Processmentioning

confidence: 99%

Review methods for clock skew measurement

Azmir

Wirastuti²,

Saputra³

2022

MATRIX

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Clock skew is the deviation of the clock from the true time, which makes it an important role in time information. Clock skew is unavoidable and causes each clock to run at a different speed, so there is a very slight difference between them, but it can still be observed properly. The known properties of clock skew make it potential research for many experts, such as Paxson, Aoki, and Huang. To produce a more accurate clock skew measurement, this study uses the ant colony optimization method by adopting the behavior of an ant colony in searching for food by choosing the path with the shortest route, which will become the cluster offset as a material for measuring clock skew. Ant colony optimization method has the most potential to be adapted in clock skew measurement because the cluster offset is not affected by the upper and the bottom outliers to produce an accurate clock skew measurement.

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“…However, the GA optimization is susceptible to local optima and exhibits slow convergence. To overcome this, an enhanced ant colony optimization (ACO) algorithm incorporating a time taboo grid strategy demonstrated success in dynamic environments (Xiong et al, 2021 ). However, ACO has a lengthy calculation cycle.…”

Section: Introductionmentioning

confidence: 99%

An autonomous mobile robot path planning strategy using an enhanced slime mold algorithm

Zheng,

Hong,

Song

et al. 2023

Front. Neurorobot.

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IntroductionAutonomous mobile robot encompasses modules such as perception, path planning, decision-making, and control. Among these modules, path planning serves as a prerequisite for mobile robots to accomplish tasks. Enhancing path planning capability of mobile robots can effectively save costs, reduce energy consumption, and improve work efficiency. The primary slime mold algorithm (SMA) exhibits characteristics such as a reduced number of parameters, strong robustness, and a relatively high level of exploratory ability. SMA performs well in path planning for mobile robots. However, it is prone to local optimization and lacks dynamic obstacle avoidance, making it less effective in real-world settings.MethodsThis paper presents an enhanced SMA (ESMA) path-planning algorithm for mobile robots. The ESMA algorithm incorporates adaptive techniques to enhance global search capabilities and integrates an artificial potential field to improve dynamic obstacle avoidance.Results and discussionCompared to the SMA algorithm, the SMA-AGDE algorithm, which combines the Adaptive Guided Differential Evolution algorithm, and the Lévy Flight-Rotation SMA (LRSMA) algorithm, resulted in an average reduction in the minimum path length of (3.92%, 8.93%, 2.73%), along with corresponding reductions in path minimum values and processing times. Experiments show ESMA can find shortest collision-free paths for mobile robots in both static and dynamic environments.

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Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment

Cited by 22 publications

References 24 publications

Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm

Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm

Review methods for clock skew measurement

An autonomous mobile robot path planning strategy using an enhanced slime mold algorithm

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