scholarly journals Path Planning for Mobile Agents Using a Genetic Algorithm with a Direction Guided Factor

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 212 ◽  
Author(s):  
Hyeok-Yeon Lee ◽  
Hyunwoo Shin ◽  
Junjae Chae
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Shortest Path ◽  
Free Space ◽  
Mobile Agents ◽  
Dimensional Space ◽  
Target Point ◽  
Complex Environments ◽  
Search Spaces ◽  
Starting Point

This paper suggests a novel methodology in collision-free shortest path planning (CFSPP) problems for mobile agents (MAs) using a method that combines a genetic algorithm (GA) and a direction factor toward a target point. In the CFSPP problem, MAs find the shortest path from the starting point to the target point while avoiding certain obstacles. The paper proposes an obstacle-based search methodology that identifies critical collision-free points adjacent to given obstacles. When critical obstacles are found via CFSPP, this study suggests favorable paths in 2-dimensional space found using the obstacle-based GA (OBGA). The OBGA has four advantages. First, it effectively narrows the search spaces compared to free space-based methodologies. It also determines shorter collision-free paths, and it only requires a short amount of time. Finally, convergence occurs more quickly than in previous studies. The proposed method also works properly in larger and more complex environments, indicating that it can be applied to more practical problems.

scholarly journals Hybrid metaheuristic approach for robot path planning in dynamic environment

2021 ◽  
Vol 10 (4) ◽  
pp. 2152-2162
Author(s):  
Lina Basem Amar ◽  
Wesam M. Jasim
Keyword(s):  
Path Planning ◽  
Hybrid Algorithm ◽  
Optimization Problem ◽  
Dynamic Environment ◽  
Target Point ◽  
Complex Environments ◽  
Robot Path Planning ◽  
Moving Obstacles ◽  
Starting Point ◽  
Robot Path

Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.

Path Planning for Spheres in Three Dimensional Environments With Low Interference Index

1994 ◽  
Author(s):  
Duane W. Storti ◽  
Debasish Dutta
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Configuration Space ◽  
Local Knowledge ◽  
Three Dimensional ◽  
Target Point ◽  
Planning Problem ◽  
Spherical Object ◽  
Starting Point ◽  
Path Planning Problem

Abstract We consider the path planning problem for a spherical object moving through a three-dimensional environment composed of spherical obstacles. Given a starting point and a terminal or target point, we wish to determine a collision free path from start to target for the moving sphere. We define an interference index to count the number of configuration space obstacles whose surfaces interfere simultaneously. In this paper, we present algorithms for navigating the sphere when the interference index is ≤ 2. While a global calculation is necessary to characterize the environment as a whole, only local knowledge is needed for path construction.

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

Robot path planning based on genetic algorithm with hybrid initialization method

2021 ◽  
pp. 1-16
Author(s):  
Zhaojun Zhang ◽  
Rui Lu ◽  
Minglong Zhao ◽  
Shengyang Luan ◽  
Ming Bu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Iteration Process ◽  
Initial Population ◽  
Local Optimum ◽  
Complex Environments ◽  
Random Initialization ◽  
Planning Problems ◽  
Complete Path

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

scholarly journals Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm

2018 ◽  
Vol 8 (9) ◽  
pp. 1425 ◽  
Author(s):  
Yang Xue ◽  
Jian-Qiao Sun
Keyword(s):  
Path Planning ◽  
Evolutionary Algorithm ◽  
Mobile Robotics ◽  
Good Choice ◽  
Target Point ◽  
Planning Problem ◽  
Multi Objective ◽  
Starting Point ◽  
Feasible Path ◽  
Path Planning Problem

Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algorithms (MOEAs). In this article, we propose a multi-objective method for solving the path planning problem. It is a population evolutionary algorithm and solves three different objectives (path length, safety, and smoothness) to acquire precise and effective solutions. In addition, five scenarios and another existing method are used to test the proposed algorithm. The results show the advantages of the algorithm. In particular, different quality metrics are used to assess the obtained results. In the end, the research indicates that the proposed multi-objective evolutionary algorithm is a good choice for solving the path planning problem.

scholarly journals Sublinear search spaces for shortest path planning in grid and road networks

2021 ◽  
Author(s):  
Johannes Blum ◽  
Stefan Funke ◽  
Sabine Storandt
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Shortest Paths ◽  
Road Networks ◽  
Grid Graphs ◽  
Search Spaces ◽  
Grid Networks ◽  
Fundamental Building Block ◽  
Important Challenge ◽  
Practical Performance

AbstractShortest path planning is a fundamental building block in many applications. Hence developing efficient methods for computing shortest paths in, e.g., road or grid networks is an important challenge. The most successful techniques for fast query answering rely on preprocessing. However, for many of these techniques it is not fully understood why they perform so remarkably well, and theoretical justification for the empirical results is missing. An attempt to explain the excellent practical performance of preprocessing based techniques on road networks (as transit nodes, hub labels, or contraction hierarchies) in a sound theoretical way are parametrized analyses, e.g., considering the highway dimension or skeleton dimension of a graph. Still, these parameters may be large in case the network contains grid-like substructures—which inarguably is the case for real-world road networks around the globe. In this paper, we use the very intuitive notion of bounded growth graphs to describe road networks and also grid graphs. We show that this model suffices to prove sublinear search spaces for the three above mentioned state-of-the-art shortest path planning techniques. Furthermore, our preprocessing methods are close to the ones used in practice and only require expected polynomial time.

scholarly journals Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm

2017 ◽  
Vol 12 (4) ◽  
pp. 26-35 ◽  
Author(s):  
Nizar Hadi Abbas ◽  
Farah Mahdi Ali
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Target Point ◽  
Autonomous Mobile Robot ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Starting Point ◽  
Bacterial Foraging Optimization Algorithm

This paper describes the problem of online autonomous mobile robot path planning, which is consisted of finding optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An enhanced algorithm for solving the problem of path planning using Bacterial Foraging Optimization algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of E-coli bacteria, was used to find the optimal path from a starting point to a target point. The proposed algorithm was demonstrated by simulations in both static and dynamic different environments. A comparative study was evaluated between the developed algorithm and other two state-of-the-art algorithms. This study showed that the proposed method is effective and produces trajectories with satisfactory results.

CAD-based CMM dimensional inspection path planning – a generic algorithm

Robotica ◽  
2001 ◽  
Vol 19 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Yueh-Jaw Lin ◽  
Rahul Mahabaleshwarkar ◽  
Elena Massina
Keyword(s):  
Path Planning ◽  
Geometric Modeling ◽  
Dimensional Space ◽  
Automatic Generation ◽  
Boundary Representation ◽  
Object Oriented Programming ◽  
Planning System ◽  
Target Point ◽  
Geometrical Structures ◽  
Dimensional Inspection

This paper describes a newly developed algorithm for CAD-based dimensional inspection path planning utilizing coordinate measuring machines (CMMs). The algorithm guarantees to provide optimum collision-free inspection probe paths by using the topological structure of the boundary representation (B-rep) solid CAD models of the inspected parts. A concept of automatic generation of optimum and collision free path in three dimensional space using available CAD database is proposed. The algorithm is generic for generating probe path over prismatic polyhedral parts. It serves as a principal part of the inspection path planning system. It is based on the modified ray tracing technique which uses a B-Rep data from any geometric modeling systems. Between start point and target point, an imaginary ray is established and if an intersection with part is encountered, an optimal detour path is created avoiding interference of the probe with the part. The generated path consists of linear segments joining start point and target point by various intermediate points. To locate these intermediate points in the consideration space, topological and geometrical structures of the part models are used at the time of decision making. To examine and implement the algorithm, a user-friendly application is developed employing AutoCAD Runtime Extension (ARX) development environment with object oriented programming (OOP) techniques, running on a Windows NT workstation. The effectiveness of the proposed algorithm is verified by the results of the implementation demonstrating optimum collision-free dimensional inspection path generation for four representative prismatic part models. All in all, this work contributes to the knowledge-base formation of automated dimensional inspection research area and paves a way for the integration of CMMs into a CAD/CAM environment, thus automate the process of design, manufacturing and quality assurance.

scholarly journals Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM

2021 ◽  
Vol 11 (16) ◽  
pp. 7599
Author(s):  
Qiang Cheng ◽  
Wei Zhang ◽  
Hongshuai Liu ◽  
Ying Zhang ◽  
Lina Hao
Keyword(s):  
Path Planning ◽  
Spline Interpolation ◽  
Optimal Solution ◽  
Gaussian Mixture ◽  
Target Point ◽  
Dijkstra Algorithm ◽  
Probabilistic Roadmap ◽  
Smooth Path ◽  
Starting Point ◽  
Feasible Path

Autonomous, flexible, and human–robot collaboration are the key features of the next-generation robot. Such unstructured and dynamic environments bring great challenges in online adaptive path planning. The robots have to avoid dynamic obstacles and follow the original task path as much as possible. A robust and efficient online path planning method is required accordingly. A method based on the Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and the Probabilistic Roadmap (PRM) is proposed to overcome the above difficulties. During the offline stage, the GMM was used to model teaching data, and it can represent the offline-demonstrated motion and constraints. The optimal solution was encoded in the mean value, while the environmental constraints were encoded in the variance value. The GMR generated a smooth path with variance as the resample space according to the GMM of the teaching data. This representation isolated the old environment model with the novel obstacle. During the online stage, a Modified Probabilistic Roadmap (MPRM) was used to plan the motion locally. Because the GMM provides the distribution of all the feasible motion, the sampling space of the MPRM was generated by the variable density resampling method, and then, the roadmap was constructed according to the Euclidean and Probability Distance (EPD). The Dijkstra algorithm was used to search for the feasible path between the starting point and the target point. Finally, shortcut pruning and B-spline interpolation were used to generate a smooth path. During the simulation experiment, two obstacles were added to the recurrent scene to indicate the difference from the teaching scene, and the GMM/GMR-MPRM algorithm was used for path planning. The result showed that it can still plan a feasible path when the recurrent scene is not the same as the teaching scene. Finally, the effectiveness of the algorithm was verified on the IRB1200 robot experiment platform.

scholarly journals A Path Planning Strategy for Multi-Robot Moving with Path-Priority Order Based on a Generalized Voronoi Diagram

2021 ◽  
Vol 11 (20) ◽  
pp. 9650
Author(s):  
Sheng-Kai Huang ◽  
Wen-June Wang ◽  
Chung-Hsun Sun
Keyword(s):  
Path Planning ◽  
Voronoi Diagram ◽  
Flat Space ◽  
Target Point ◽  
Planning Strategy ◽  
Navigation Strategy ◽  
Shortest Distance ◽  
Starting Point ◽  
Priority Order ◽  
Generalized Voronoi Diagram

This paper proposes a new path planning strategy called the navigation strategy with path priority (NSPP) for multiple robots moving in a large flat space. In the space, there may be some static or/and dynamic obstacles. Suppose we have the path-priority order for each robot, then this article aims to find an efficient path for each robot from its starting point to its target point without any collision. Here, a generalized Voronoi diagram (GVD) is used to perform the map division based on each robot’s path-priority order, and the proposed NSPP is used to do the path planning for the robots in the space. This NSPP can be applied to any number of robots. At last, there are several simulations with a different number of robots in a circular or rectangular space to be shown that the proposed method can complete the task effectively and has better performance in average trajectory length than those by using the benchmark methods of the shortest distance algorithm (SDA) and reciprocal orientation algorithm (ROA).

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