Development of Swarm Intelligence Leader-Vicsek-Model for Multi-AGV Path Planning

2021 ◽  
Author(s):  
Shiwei Lin ◽  
Ang Liu ◽  
Xiaoying Kong ◽  
Jack Wang
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Vicsek Model

scholarly journals Novel Swarm Intelligence Algorithm for Global Optimization and Multi-UAVs Cooperative Path Planning: Anas Platyrhynchos Optimizer

2020 ◽  
Vol 10 (14) ◽  
pp. 4821
Author(s):  
Yong Zhang ◽  
Pengfei Wang ◽  
Liuqing Yang ◽  
Yanbin Liu ◽  
Yuping Lu ◽  
...  
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Local Development ◽  
Anas Platyrhynchos ◽  
Intelligent Algorithm ◽  
Intelligence Algorithm ◽  
Engineering Problems ◽  
The Mathematical Model ◽  
Swarm Intelligence Algorithm

In this study, a novel type of swarm intelligence algorithm referred as the anas platyrhynchos optimizer is proposed by simulating the cluster action of the anas platyrhynchos. Starting from the core of swarm intelligence algorithm, on the premise of the use of few parameters and ease in implementation, the mathematical model and algorithm flow of the anas platyrhynchos optimizer are given, and the balance between global search and local development in the algorithm is ensured. The algorithm was applied to a benchmark function and a cooperative path planning solution for multi-UAVs as a means of testing the performance of the algorithm. The optimization results showed that the anas platyrhynchos optimizer is more superior in solving optimization problems compared with the mainstream intelligent algorithm. This study provides a new idea for solving more engineering problems.

Optimal Path Planning Using Swarm Intelligence Based Hybrid Techniques

2019 ◽  
Vol 16 (9) ◽  
pp. 3717-3727
Author(s):  
Monica Sood ◽  
Sahil Verma ◽  
Vinod Kumar Panchal ◽  
Kavita
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Research Work ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Hybrid Techniques ◽  
Different Types

The planning of optimal path is an important research domain due to vast applications of optimal path planning in the robotics, simulation and modeling, computer graphics, virtual reality estimation and animation, and bioinformatics. The optimal path planning application demands to determine the collision free shortest and optimal path. There can be numerous possibilities that to find the path with optimal length based on different types of available obstacles during the path and different types of workspace environment. This research work aims to identify the optimum path from the initial source-point to final point for the unknown workspace environment consists of static obstacles. For this experimentation, swarm intelligence based hybrid concepts are considered as the work collaboration and intelligence behavior of swarm agents provides the resourceful solution of NP hard problems. Here, the hybridization of concepts makes the solution of problem more efficient. Among swarm intelligence concepts, cuckoo search (CS) algorithm is one of the efficient algorithms due to clever behavior and brood parasitic property of cuckoo birds. In this research work, two hybrid concepts are proposed. First algorithm is the hybridized concept of cuckoo search with bat algorithm (BA) termed as CS-BAPP. Another algorithm is the hybridized concept of cuckoo search with firefly algorithm (FA) termed as CS-FAPP. Both algorithms are initially tested on the benchmarks functions and applied to the path planning problem. For path planning, a real time dataset area of Alwar region situated at Rajasthan (India) is considered. The selected region consists of urban and dense vegetation land cover features. The results for the optimal path planning on Alwar region are assessed using the evaluation metrics of minimum number of iterations, error rate, success rate, and simulation time. Moreover, the results are also compared with the individual FA, BA, and CS along with the comparison of hybrid concepts.

Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning

2014 ◽  
Vol 7 (1) ◽  
pp. 24-37 ◽  
Author(s):  
Haibin Duan ◽  
Peixin Qiao
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Implementation Process ◽  
Robot Path Planning ◽  
Operator Model ◽  
Content Type ◽  
De Algorithm ◽  
The Mathematical Model ◽  
Planning Problems ◽  
Robot Path

Purpose – The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems. Design/methodology/approach – The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted. Findings – The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases. Originality/value – In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is presented based on magnetic field and sun, while landmark operator model is designed based on landmarks. The authors also applied this newly proposed PIO algorithm for solving air robot path planning problems.

scholarly journals Implementation of Swarm Intelligence Algorithms for Path Planning

2021 ◽  
Vol 1831 (1) ◽  
pp. 012008
Author(s):  
Alex T Mathew ◽  
Ajay Paul ◽  
Akshay Rojan ◽  
Amith Thomas
Keyword(s):  
Path Planning ◽  
Swarm Intelligence

scholarly journals A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

2021 ◽  
Vol 9 (11) ◽  
pp. 1243
Author(s):  
Charis Ntakolia ◽  
Dimitrios V. Lyridis
Keyword(s):  
Fuzzy Logic ◽  
Path Planning ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Computation Time ◽  
Computational Effort ◽  
Unmanned Vehicles ◽  
Maritime Transportation ◽  
Solution Quality ◽  
Multi Objective

Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs in maritime transportation. However, most of the traditional path planning approaches include single-objective approaches that mainly find the shortest path. Dynamic and complex environments impose the need for multi-objective path planning where an optimal path should be found to satisfy contradicting objective terms. To this end, a swarm intelligence graph-based pathfinding algorithm (SIGPA) has been proposed in the recent literature. This study aims to enhance the performance of SIGPA algorithm by integrating fuzzy logic in order to cope with the multiple objectives and generate quality solutions. A comparative evaluation is conducted among SIGPA and the two most popular fuzzy inference systems, Mamdani (SIGPAF-M) and Takagi–Sugeno–Kang (SIGPAF-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. SIGPA remains a reliable approach for real-time applications due to low computational effort; SIGPAF-M generates better paths; and SIGPAF-TSK reaches a better trade-off among solution quality and computation time.

Autonomous Robot Path Planning Based on Swarm Intelligence and Stream Functions

2007 ◽  
pp. 277-284 ◽  
Author(s):  
Chengyu Hu ◽  
Xiangning Wu ◽  
Qingzhong Liang ◽  
Yongji Wang
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Autonomous Robot ◽  
Robot Path Planning ◽  
Stream Functions ◽  
Robot Path

scholarly journals Robot Path Planning based on Swarm Intelligence

2014 ◽  
Vol 7 (7) ◽  
pp. 15-32 ◽  
Author(s):  
Xuesong Yan ◽  
Qinghua Wu ◽  
Chengyu Hu ◽  
Hong Yao ◽  
Yuanyuan Fan ◽  
...  
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Robot Path Planning ◽  
Robot Path

Optimal path planning for intelligent automated wheelchair using DDSRPSO

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kalaipriyan Thirugnanasambandam ◽  
Raghav R.S. ◽  
Jayakumar Loganathan ◽  
Ankur Dumka ◽  
Dhilipkumar V.
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Swarm Intelligence ◽  
Cooperative Behavior ◽  
Optimal Path ◽  
Particle Swarm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Optimal Path Planning

Purpose This paper aims to find the optimal path using directionally driven self-regulating particle swarm optimization (DDSRPSO) with high accuracy and minimal response time. Design/methodology/approach This paper encompasses optimal path planning for automated wheelchair design using swarm intelligence algorithm DDSRPSO. Swarm intelligence is incorporated in optimization due to the cooperative behavior in it. Findings The proposed work has been evaluated in three different regions and the comparison has been made with particle swarm optimization and self-regulating particle swarm optimization and proved that the optimal path with robustness is from the proposed algorithm. Originality/value The performance metrics used for evaluation includes computational time, success rate and distance traveled.

Swarm Intelligence Optimization Techniques in Mobile Path Planning - A Review

2018 ◽  
Vol 37 ◽  
pp. 62-71 ◽  
Author(s):  
Chika O. Yinka-Banjo ◽  
Ukamaka Hope Agwogie
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Dynamic Environment ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Crucial Problem ◽  
Swarm Intelligence Optimization ◽  
Present World

In the present world, mobile robot has been widely used for many functions across different areas of life. These mobile robots can be engaged in a static or dynamic environment where they are expected to accomplish a task optimally against all odds. Path planning for mobile robot is a very crucial problem in robotics that has been greatly researched upon; it is aimed at finding an optimal path in a given environment from a start point to the goal point. Several techniques have been employed in solving this crucial problem. These techniques are broadly classified as classical and heuristics. The Swarm Intelligence Techniques form a sub-class of the heuristics approach. The aim of this research is to review the swarm intelligence techniques in solving the mobile robot path planning problem. The drawbacks and merits of each of the techniques were discussed and a comparative analysis was given.

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