Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

Dai, Xiaolin; Long, Shijun; Zhang, Zhiwen; Gong, Dunwei

doi:10.3389/fnbot.2019.00015

Cited by 167 publications

(89 citation statements)

References 35 publications

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“…It endorses that it utilizes the network coding resources in better way. It is now used in latest projects like path finding for unmanned vehicles [44] and Path Planning for Mobile Robots [9].…”

Section: Gray Hole Attackmentioning

confidence: 99%

“…As this study is focusing on prevention of Black Hole Attacks in MANET, the path finding and route optimization technique can be selected among these metaheuristics simple, basic and advanced techniques of Ant Colony Optimization [9], Particle Swarm Optimization (PSO) [26], External Optimization [45,46], Large Neighborhood Search [17] and Neuro-Heuristic Methods [31].…”

Section: Objectives and Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Black Hole Attack Prevention in Mobile Ad-hoc Network (MANET) using Ant Colony Optimization Technique

Khan

Aslam

Akhtar

et al. 2020

ITC

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The Mobile ad-hoc networks are auto configured systems where hubs can travel towards any direction. It doesn’t rely on any other frame to build the framework. Because of its versatility, the black hole is a real security issue to be settled. It occurs once a malignant hub referred to as black hole goes into the framework. Black hole center point exhibits its fake lead among the methodology after all disclosure. Currently, varied strategies are made arrangements for Mobile ad-hoc networks. Regardless, at interims seeing Black hole or fake hubs, the entire Mobile ad-hoc frameworks are exposed to various sorts of assaults or network attacks. Among these issues, a black hole center points plugs itself of getting a most nearest targeted hub, whose data packet must be constrained to drop. In this flooding procedures, if the communication from the genuine hub accomplishes later than the fake hub reply as requested by the standard hub. A false communication path is framed through a fake center hub. A perfect method is one in which the package reach to target with less delay and lesser overhead. In this paper we'll talk about the implementation of Ant Colony Optimization Technique and Repetitive Route Configuration with Reactive Routing Protocol for obstruction of black hole Attack in Mobile ad-hoc networks.

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Section: Gray Hole Attackmentioning

confidence: 99%

Section: Objectives and Motivationmentioning

confidence: 99%

Black Hole Attack Prevention in Mobile Ad-hoc Network (MANET) using Ant Colony Optimization Technique

Khan

Aslam

Akhtar

et al. 2020

ITC

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show abstract

“…However, the recent investigation has been improved by adopting different techniques. For example, Dai et al [16] implemented the A* characteristics into an ant algorithm and Yang et al [17] presented an efficient double layer ant colony, which is called DL-ACO. This novel concept uses two ant colony running independently and successively.…”

Section: Of 19mentioning

confidence: 99%

“…Ant algorithm with A* characteristic [16] A* accelerates the ant colony conversion and increases the smoothness of global path.…”

Section: Reference Advantages Weaknessmentioning

confidence: 99%

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

et al. 2019

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The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the α D , β D , and γ D parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.

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“…ACA (ACO) is used for path optimization and obstacle avoidance planning for robots or UAVs [33]- [39]. Variants of ACA reduce the computing costs and training speeds [28], [40], [41].…”

Section: Introductionmentioning

confidence: 99%

Positioning and Sequence Planning of Drilling Boreholes in Hard Rock Roadway

Cui

Liu

et al. 2020

IEEE Access

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The safety and efficiency of hard rock roadway excavation are the two sides of a coin limiting the implementation of roadway construction. It is an effective way to improve the performance in hard rock tunneling to effectively release the internal stress in rock mass by arranging pre-prepared boreholes in rock section. In order to improve the rock breaking performance and tunneling efficiency, we analyze the borehole positioning accuracy and explore boreholes sequence planning problem in hard rock excavation simulatively and experimentally. The kinematics model of drilling mechanism is established based on the D-H method. RBF neural network algorithm is adopted to accurately position borehole. The results show that accuracy of simulated borehole positioning is controlled at 1.5% while the accuracy is 5.6% of experiment. The Ant Colony Algorithm (ACA) is employed to optimize the sequence planning of drilling boreholes with the optimization goals of minimum moving distance and minimum swing angle separately. Simulation results show that rock-drilling with the minimum swing angle produces smaller positioning deviation and higher efficiency, which is a practical automatic roadway borehole drilling method. INDEX TERMS Borehole positioning, RBF neural network, sequence planning, ant colony algorithm (ACA).

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Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

Cited by 167 publications

References 35 publications

Black Hole Attack Prevention in Mobile Ad-hoc Network (MANET) using Ant Colony Optimization Technique

Black Hole Attack Prevention in Mobile Ad-hoc Network (MANET) using Ant Colony Optimization Technique

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

Positioning and Sequence Planning of Drilling Boreholes in Hard Rock Roadway

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