Research on Path Planning Method of Humanoid Robot Based on Improved Genetic Algorithm

With the rapid development of the Internet, e-commerce business has gradually emerged. However, its logistics distribution route planning method has problems such as redundancy of logistics data, which cannot achieve centralized planning of distribution paths, resulting in low e-commerce logistics distribution efficiency and long distribution distances, higher cost. Therefore, in order to improve the ability of logistics distribution path planning, this paper designs an e-commerce logistics distribution path planning method based on improved genetic algorithm. Optimize the analysis of e-commerce logistics distribution nodes, establish a modern logistics distribution system, and optimize the total transportation time and transportation cost under the location model of the logistics distribution center. Using hybrid search algorithm and improved genetic algorithm parameters, an improved genetic algorithm distribution path planning model is established to select the optimal path of logistics distribution, and realize e-commerce logistics distribution path with high accuracy, low error and good convergence. planning. According to the experimental results, the method in this paper can effectively shorten the distance of e-commerce logistics distribution path, reduce the number of distribution vehicles, reduce distribution costs, improve distribution efficiency, and effectively achieve centralized planning of logistics distribution. Therefore, the e-commerce logistics distribution route planning method based on improved genetic algorithm has high practical application value.

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A novel evacuation path planning method based on improved genetic algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211214 ◽

2021 ◽

pp. 1-11

Author(s):

Longzhen Zhai ◽

Shaohong Feng

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Path Planning ◽

Simulated Annealing Algorithm ◽

Planning Method ◽

Improved Genetic Algorithm ◽

Genetic Operator ◽

Probability Operator ◽

Annealing Algorithm ◽

Evacuation Route

In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm.

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Path Planning of Mobile Robots based on Improved Genetic Algorithm

Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence ◽

10.1145/3438872.3439054 ◽

2020 ◽

Author(s):

Jia Liu ◽

Zhikang Chen ◽

Yunxi Zhang ◽

Weixun Li

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Mobile Robots ◽

Improved Genetic Algorithm

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An efficient intelligent decision method for bionic motion unmanned system

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/09596518211065581 ◽

2021 ◽

pp. 095965182110655

Author(s):

Haipeng Chen ◽

Wenxing Fu ◽

Yuze Feng ◽

Jia Long ◽

Kang Chen

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Path Planning ◽

Motion Control ◽

Control Method ◽

Improved Genetic Algorithm ◽

Decision Method ◽

Multiple Traveling Salesman Problem ◽

Intelligent Decision ◽

Unmanned System

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

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A Static Environment-Based Path Planning Method by Using Genetic Algorithm

2010 International Conference on Computing, Control and Industrial Engineering ◽

10.1109/ccie.2010.220 ◽

2010 ◽

Cited By ~ 12

Author(s):

Zhigang Yao ◽

Lianyang Ma

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Planning Method

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Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-11-2018-0032 ◽

2019 ◽

Vol 7 (3) ◽

pp. 112-119 ◽

Cited By ~ 3

Author(s):

Asita Kumar Rath ◽

Dayal R. Parhi ◽

Harish Chandra Das ◽

Priyadarshi Biplab Kumar ◽

Manoj Kumar Muni ◽

...

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Path Planning ◽

Humanoid Robot ◽

Fuzzy Logic Controller ◽

Target Location ◽

Target Position ◽

Content Type ◽

Hybrid Controller ◽

Fuzzy Genetic

Purpose Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues. Design/methodology/approach Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup. Findings By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors. Originality/value Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

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