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).

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.

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 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 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.

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.

Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Robotica ◽  
2019 ◽  
Vol 38 (2) ◽  
pp. 235-255 ◽  
Author(s):  
Raouf Fareh ◽  
Mohammed Baziyad ◽  
Mohammad H. Rahman ◽  
Tamer Rabie ◽  
Maamar Bettayeb
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Control Strategy ◽  
Wheeled Mobile Robot ◽  
Computational Time ◽  
Planning Strategy ◽  
Starting Point ◽  
Planning Algorithm ◽  
Path Efficiency ◽  
Path Planning Algorithm

SummaryThis paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

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 Vehicle Motion Planning for the Visually Challenged People Using Ant Colony Optimization

YMER Digital ◽  
2021 ◽  
Vol 20 (12) ◽  
pp. 583-588
Author(s):  
Dr. S Prasanna ◽  
◽  
Dr. T Indumathi ◽  
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Obstacle Detection ◽  
Ant Colony ◽  
Sensor Technology ◽  
Target Point ◽  
Planning Problem ◽  
Smart Watch ◽  
Starting Point ◽  
Visually Challenged

This paper presents a novel proposal to solve the problem of obstacle detection and path planning for visually challenged people based on simple Ant Colony Optimization Meta-heuristic (SACO-MH). The mission of the path planning problem is to enable the vehicle to move from the starting point to the target point while satisfying certain constraints. Constraint conditions are: not a collision with known or unknown obstacles, away from the obstacle as far as possible, determines the shortest path, shortest time and so on. Obstacle detection is made with the help of sensor technology and it is intimated to the user with the help of a smart watch. A voice based navigation system guides the user

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