Multi-robot path planning in a dynamic environment using improved gravitational search algorithm

Das, Pradipta Kumar; Behera, H. S.; Jena, Prabir Kumar; Panigrahi, B. K.

doi:10.1016/j.jesit.2015.12.003

Cited by 52 publications

(22 citation statements)

References 17 publications

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“…Virtual map and the real map Accelerated convergence speed [12] Dynamic Fuzzy logic based path planning Wireless sensor networks in MATLAB Localization ratio and localization accuracy [13] Dynamic fuzzy-logic-ant colony system Regions of London, United Kingdom Efficient route selection [14] Ant colony and fuzzy logic Simulated maps in MATLAB Shortest path in minimum time [15] Fuzzy logic ant colony optimization Simulated road networks Shortest path length [16] Cuckoo optimization algorithm Simulated scenarios of size 20 × 20, 100 × 100 and 200 × 200 Safe, smooth, and collision-free path [17] A visual-inertial navigation system Urban areas of Hong Kong Effective mitigation of dynamic objects and improved accuracy [18] Fuzzy-genetic algorithm (GA) with three path concept Simulated maps Computationally efficient [19] Improved gravitational search Real-time navigation using Khepera III mobile robot The safe and shortest path [20] Genetic Zhang et al [25] made an extensive survey on path planning approaches for mobile robots. In their work, they emphasized the advantages of using a genetic algorithm, particle swarm optimization, ant colony optimization, and artificial potential fields in path planning with future directions.…”

Section: Ref #mentioning

confidence: 99%

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A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

et al. 2021

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Path planning can be perceived as a combination of searching and executing the optimal path between the start and destination locations. Deliberative planning capabilities are essential for the motion of autonomous unmanned vehicles in real-world scenarios. There is a challenge in handling the uncertainty concerning the obstacles in a dynamic scenario, thus requiring an intelligent, robust algorithm, with the minimum computational overhead. In this work, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time. The ant colony system’s pheromone update mechanism was enhanced with a sigmoid gain function for effective exploitation during path planning. Collision avoidance was achieved through the proposed fuzzy logic control. The results were validated using occupancy grids of variable size, and the results were compared against existing methods concerning performance metrics, namely, time and length. The consistency of the algorithm was also analyzed, and the results were statistically verified.

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Section: Ref #mentioning

confidence: 99%

“…Das et al [29] hybridized an improved particle swarm optimization and a gravitational search algorithm to develop a path planning algorithm for multiple robots. Simulations were performed in the Khepera environment [19] to test the efficiency.…”

Section: Ref #mentioning

confidence: 99%

A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

et al. 2021

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“…6,7 Through the joint research of robot technology and computer vision to study human behavior recognition, analyzing and identifying different actions from video data are very important in many visual applications. 8 For example, in the field of security monitoring, human motion recognition is used in video surveillance systems, which can automatically warn of emergencies, which is of great significance in urban law and order, fire protection, and traffic dispatching; in the field of intelligent monitoring, it can fall in the elderly or children or send out an alert when a dangerous behavior is made for timely rescue; to a certain extent, it can solve the problem that some contemporary people have no time to take care of the elderly and children; and in the field of health care, by studying the performance of human gait, it can hurt the legs of patients. The degree of analysis is used to develop the best treatment plan.…”

Section: Introductionmentioning

confidence: 99%

Human motion recognition based on limit learning machine

Chen

Zhao

et al. 2020

International Journal of Advanced Robotic Systems

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With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action feature descriptors, this article makes improvements to features and classifiers and performs experiments on the Microsoft model specific register (MSR)-Action3D data set and the Bonn University high density metal (HDM05) motion capture data set. Based on displacement covariance descriptor and direction histogram descriptor, this article described both combine to produce a new combination; the description can statically reflect the joint position relevant information and at the same time, the change information dynamically reflects the joint position, uses the extreme learning machine for classification, and gets better recognition result. The experimental results show that the combined descriptor and extreme learning machine recognition rate on these two data sets is significantly improved by about 3% compared with the existing methods.

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“…As far as current research is concerned, combining artificial intelligence and mobile robot research is an inevitable result of technological development. However, the research on the path problem of mobile robots alone and combined with artificial intelligence algorithms is relatively rare [4]. Some scholars have improved the ant colony algorithm and applied it to the path calculation and planning of mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Application of Path Planning for Intelligent Mechanical Robot Under Embedded Real-Time System

2020

IJOMAM

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Objective: The paper is to study the path planning of the intelligent mechanical robot under the embedded real-time system, to provide theoretical support for the path planning problems of mobile robots. Method: In this paper, the embedded system is applied to the path planning of the mobile robot, and it is used as the processor of the mobile robot. The path planning of the mobile robot and algorithms to avoid obstacles are also studied. Also, this paper proposes an improved A* algorithm. It will build a specific environment grid based on the existing environment materials and use the improved A* algorithm to analyze each node in detail to explore the most suitable and fastest path. Results: The total turning angle of the mobile robot using the primary A* algorithm is 180° and the path length is 10.07m. The total turning angle of the mobile robot using the secondary A* algorithm is 112.62°, and the path length is 9.84m. According to the experimental results, compared with the primary A* algorithm, the secondary A* algorithm can effectively reduce the path length and unnecessary turning of the mobile robot during the travel. The path length is reduced by 2.21% and the turning angle is reduced by 37.43%. The secondary A* algorithm has effectively improved the turning angle of the mobile robot and has greatly optimized the travel path of the mobile robot. Conclusion: The improved A* algorithm in this paper can effectively plan the travel path of the mobile robot, and ensure the efficiency and correctness of the planned path. The path planning and design error of the mobile robot in this paper is relatively low, and it can perform better path planning.

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Multi-robot path planning in a dynamic environment using improved gravitational search algorithm

Cited by 52 publications

References 17 publications

A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

Human motion recognition based on limit learning machine

Application of Path Planning for Intelligent Mechanical Robot Under Embedded Real-Time System

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