Ground Robot Path Planning Based on Simulated Annealing Genetic Algorithm

Wang, Lanfei; Guo, Jun; Wang, Qu; Kan, Jiangming

doi:10.1109/cyberc.2018.00081

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…SA (Wang et al, 2018 ) algorithm is an efficient approximation algorithm for large-scale combinatorial optimization problems. It uses the neighborhood structure of the solution space to perform a stochastic search.…”

Section: Related Workmentioning

confidence: 99%

Optimization of robotic path planning and navigation point configuration based on convolutional neural networks

Wu,

Li,

et al. 2024

Front. Neurorobot.

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This study introduces a novel approach for enhancing robotic path planning and navigation by optimizing point configuration through convolutional neural networks (CNNs). Faced with the challenge of precise area coverage and the inefficiency of traditional traversal and intelligent algorithms (e.g., genetic algorithms, particle swarm optimization) in point layout, we proposed a CNN-based optimization model. This model not only tackles the issues of speed and accuracy in point configuration with Gaussian distribution characteristics but also significantly improves the robot's capability to efficiently navigate and cover designated areas with high precision. Our methodology begins with defining a coverage index, followed by an optimization model that integrates polygon image features with the variability of Gaussian distribution. The proposed CNN model is trained with datasets generated from systematic point configurations, which then predicts optimal layouts for enhanced navigation. Our method achieves an experimental result error of <8% on the test dataset. The results validate effectiveness of the proposed model in achieving efficient and accurate path planning for robotic systems.

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Section: Related Workmentioning

confidence: 99%

Optimization of robotic path planning and navigation point configuration based on convolutional neural networks

Wu,

Li,

et al. 2024

Front. Neurorobot.

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“…e path evaluation function is calculated according to formula (9), and the pheromone increment is calculated according to formula (8).…”

Section: Application Of Improved Ant Colony Algorithm In Path Planningmentioning

confidence: 99%

“…In recent years, some researchers have adopted bionic intelligent optimization algorithms to solve the problem of path planning. ese bionic intelligent optimization algorithms mainly include ant colony algorithm [5], genetic algorithm [6], particle swarm optimization algorithm [7], immune algorithm [8], simulated annealing algorithm [9], and the combined optimization algorithm among the algorithms [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Wang

Zhao

et al. 2021

Journal of Robotics

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Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

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“…A large number of studies have shown that the overall optimization of positioning, allocation, and path holds the key to solving LRP. Previously, scholars were limited to the study of a single distribution center, employing the total cost function to describe the path cost [2]. By comparing the total cost function and the potential location path cost, Webb pointed out that the path cost cannot be represented by the approximate total cost function at a certain moment [3].…”

Section: Introductionmentioning

confidence: 99%

Two‐Layer Location‐Routing Problem Based on Heuristic Hybrid Algorithm

Geng

Zhu

2023

Mathematical Problems in Engineering

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Location-routing problem (LRP) thoroughly considers location allocation problem (LAP) and vehicle routing problem (VRP) which has been an integral part applied in modern logistics. A number of researchers at home and aboard have put forward their views by establishing fine models. On the basis of studying the previous research results by classification, summary, and comparative analysis, this study hence proposes a new solution-fuzzy clustering model and algorithm to resolve two-layer location-routing problem based on a heuristic hybrid algorithm: Designing a hybrid genetic and simulated annealing algorithm (GASA) to optimize the initial value of the fuzzy C-means clustering algorithm (FCM); considering the roving visit characteristics of vehicles to design the path by employing a special VRP problem—the multiple traveling salesman problem (MTSP). Theoretical analysis and experimental results show that the algorithm used in this study has the advantages of fast convergence speed and less iterations, which significantly improve the quality of the initial solution of FCM in LAP, shorten the vehicle patrol cycle in VRP to a great extent, improve the vehicle utilization, and save the vehicle patrol costs. A specific example is programmed by MATLAB to verify the feasibility of this method.

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Ground Robot Path Planning Based on Simulated Annealing Genetic Algorithm

Cited by 9 publications

References 9 publications

Optimization of robotic path planning and navigation point configuration based on convolutional neural networks

Optimization of robotic path planning and navigation point configuration based on convolutional neural networks

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Two‐Layer Location‐Routing Problem Based on Heuristic Hybrid Algorithm

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