Unmanned vehicle path planning using a novel ant colony algorithm

Yue, Longwang; Chen, Hanning

doi:10.1186/s13638-019-1474-5

Cited by 51 publications

(30 citation statements)

References 12 publications

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“…Coordinating a swarm of uavs to provide continuous coverage of an area of interest, identified as an intractable problem [38], can be effectively handled by evolutionary bio-inspired techniques. An adequate deployment of a swarm of uavs can be obtained by applying particle swarm optimization techniques [7] while path planning for multiobjective missions can be achieved using genetic algorithms [33,40] and artificial ant colony optimization [49] methods. A leader-follower coalition formation in swarms with large number of uavs, each with limited communication and energy capabilities, was proposed in [29] by employing quantum genetic algorithms.…”

Section: Bio-inspired Computation For Swarms and Uavsmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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Autonomous unmanned aerial vehicles (uavs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, uav synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous uav to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (em) information. Each uav using our flight control algorithms positions itself such that the swarm maintains mobile ad-hoc network (manet) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile em transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected manet, promote a uniform network spreading, while avoiding overcrowding with other swarm members. We also prove that they maintain manet connectivity and, at the same time, they can lead a swarm of autonomous uavs to follow or avoid an em transmitter. Simulation experiments in opnet modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile em source and maintain manet connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile em transmitters.

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Section: Bio-inspired Computation For Swarms and Uavsmentioning

confidence: 99%

Artificial intelligence and game theory controlled autonomous UAV swarms

Kusyk

Samoylov

et al. 2020

Evol. Intel.

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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%

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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“…After years of research and exploration, many control algorithms have emerged. Currently, widely used path planning algorithms include ant colony algorithms [4][5][6], bee swarm algorithms [7][8], the virtual artificial potential field method [9][10], quasi-annealing algorithms [11], Neural network algorithms [12][13][14] and particle swarm optimization [15][16][17]. However, the most commonly used task allocation strategies are artificial self-organizing neural network algorithms (SOM) [18] and tree structure algorithms [19].…”

Section: Introductionmentioning

confidence: 99%

Multi-AUV Collaborative Operation Based on Time-Varying Navigation Map and Dynamic Grid Model

Chen

Bai

et al. 2020

IEEE Access

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In a dynamic and complex environment, to improve the cooperative operation efficiency of multiple AUV groups, a bionic neural wave network (BNWN) algorithm, and a velocity vector synthesis (VVS) algorithm are proposed. A strategy of space decomposition and node space recursion is adopted to provide dynamic navigation maps for AUV monomers and to modularize the tasks. A closed boundary function is introduced to construct a dynamic grid model to autonomously avoid obstacles with multiple moving forms. The results of three sets of simulation experiments show that the number of changes in direction, the total path length, and the collision rate of AUV individuals are greatly reduced. These results prove that the proposed algorithm has high autonomy and strong adaptability.

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Unmanned vehicle path planning using a novel ant colony algorithm

Cited by 51 publications

References 12 publications

Artificial intelligence and game theory controlled autonomous UAV swarms

Artificial intelligence and game theory controlled autonomous UAV swarms

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

Multi-AUV Collaborative Operation Based on Time-Varying Navigation Map and Dynamic Grid Model

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