scholarly journals Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles

2021 ◽  
Vol 11 (16) ◽  
pp. 7378
Author(s):  
Hongchao Zhuang ◽  
Kailun Dong ◽  
Yuming Qi ◽  
Ning Wang ◽  
Lei Dong
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Working Conditions ◽  
Planning Method ◽  
Grid Map ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Global Planning ◽  
Path Planning Algorithm

In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots.

scholarly journals Rapid global path planning algorithm for unmanned surface vehicles in large-scale and multi-island marine environments

2021 ◽  
Vol 7 ◽  
pp. e612
Author(s):  
Dong Wang ◽  
Jie Zhang ◽  
Jiucai Jin ◽  
Deqing Liu ◽  
Xingpeng Mao
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Distance Constraint ◽  
Marine Environments ◽  
Fast Marching ◽  
Unmanned Surface Vehicles ◽  
Grid Map ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

A global path planning algorithm for unmanned surface vehicles (USVs) with short time requirements in large-scale and complex multi-island marine environments is proposed. The fast marching method-based path planning for USVs is performed on grid maps, resulting in a decrease in computer efficiency for larger maps. This can be mitigated by improving the algorithm process. In the proposed algorithm, path planning is performed twice in maps with different spatial resolution (SR) grids. The first path planning is performed in a low SR grid map to determine effective regions, and the second is executed in a high SR grid map to rapidly acquire the final high precision global path. In each path planning process, a modified inshore-distance-constraint fast marching square (IDC-FM2) method is applied. Based on this method, the path portions around an obstacle can be constrained within a region determined by two inshore-distance parameters. The path planning results show that the proposed algorithm can generate smooth and safe global paths wherein the portions that bypass obstacles can be flexibly modified. Compared with the path planning based on the IDC-FM2 method applied to a single grid map, this algorithm can significantly improve the calculation efficiency while maintaining the precision of the planned path.

scholarly journals Desempeño en métodos de navegación autónoma para robots móviles

Minerva ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 19-29
Author(s):  
Gabriela Alvarez ◽  
Omar Flor
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Probabilistic Neural Network ◽  
Autonomous Systems ◽  
Random Trees ◽  
Annual Conference ◽  
Planning Algorithm ◽  
Path Planning Algorithm

En este trabajo se presenta una comparación de los tiempos de respuesta, optimización de la ruta y complejidad del grafo en métodos de planificación de trayectoria para robots móviles autónomos. Se contrastan los desarrollos de Voronoi, Campos potenciales, Roadmap probabilístico y Descomposición en celdas para la navegación en un mismo entorno y validándolos para un número variable de obstáculos. Las evaluaciones demuestran que el método de generación de trayectoria por Campos Potenciales, mejora la navegación respecto de la menor ruta obtenida, el método Rapidly Random Tree genera los grafos de menor complejidad y el método Descomposición en celdas, se desempeña con menor tiempo de respuesta y menor coste computacional. Palabras Clave: optimización, trayectoria, métodos de planificación, robots móviles. Referencias [1]H. Ajeil, K. Ibraheem, A. Sahib y J. Humaidi, “Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm, ” Applied Soft Computing, vol. 89, April 2020. [2]K.Patle, G. Babu, A. Pandey, D.R.K. Parhi y A. Jagadeesh, “A review: On path planning strategies for navigation of mobile robot,” Defence Technology, vol. 15, pp. 582-606, August 2019. [3]T. Mack, C. Copot, D. Trung y R. De Keyser, “Heuristic approaches in robot path planning: A survey,” Robotics and Autonomous Systems, vol. 86, pp. 13-28, December 2016. [4]L. Zhang, Z. Lin, J. Wang y B. He, “Rapidly-exploring Random Trees multi-robot map exploration under optimization framework,” Robotics and Autonomous Systems, vol. 131, 2020. [5]S. Khan y M. K. Ahmmed, "Where am I? Autonomous navigation system of a mobile robot in an unknown environment," 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 56-61, December 2016. [6]V. Castro, J. P. Neira, C. L. Rueda, J. C. Villamizar y L. Angel, "Autonomous Navigation Strategies for Mobile Robots using a Probabilistic Neural Network (PNN)," IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 2795-2800, Taipei, 2007. [7]Y. Li, W. Wei, Y. Gao, D. Wang y C. Fan, “PQ-RRT*: An improved path planning algorithm for mobile robots,” Expert Systems with Applications, vol. 152, August 2020. [8]A. Muñoz, “Generación global de trayectorias para robots móviles, basada en curvas betaspline,” Dep. Ingeniería de Sistemas y Automática Escuela Técnica Superior de Ingeniería Universidad de Sevilla, 2014. [9]H. Montiel, E. Jacinto y H. Martínez, “Generación de Ruta Óptima para Robots Móviles a Partir de Segmentación de Imágenes,” Información Tecnológica, vol. 26, 2015. [10] C. Expósito, “Los diagramas de Vornooi, la forma matemática de dividir el mundo,” Dialnet, Diciembre 2016.

Comparison between Waveform and Bug Path Planning Algorithm for Mobile Robot

2014 ◽  
Vol 607 ◽  
pp. 774-777
Author(s):  
Swee Ho Tang ◽  
Che Fai Yeong ◽  
Eileen Lee Ming Su
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Execution Time ◽  
Path Length ◽  
Simulation Software ◽  
Breadth First Search ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Grid Based

Mobile robots frequently find themselves in a circumstance where they need to find a trajectory to another position in their environment, subject to constraints postured by obstacles and the capabilities of the robot itself. This study compared path planning algorithms for mobile robots to move efficiently in a collision free grid based static environment. Two algorithms have been selected to do the comparison namely wavefront algorithm and bug algorithm. The wavefront algorithm involves a breadth-first search of the graph beginning at the goal position until it reaches the start position. The bug algorithm uses obstacles borders as guidance toward a goal with restricted details about the environment. The algorithms are compared in terms of parameters such as execution time of the algorithm and planned path length by using Player/Stage simulation software. Results shown that wavefront algorithm is a better path planning algorithm compared to bug algorithm in static environment.

Development and analysis of DAYANI arc contour intelligent technique for navigation of two-wheeled mobile robot

2018 ◽  
Vol 45 (5) ◽  
pp. 688-702 ◽  
Author(s):  
Dayal R. Parhi ◽  
Animesh Chhotray
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Weight Functions ◽  
Feasible Point ◽  
Content Type ◽  
Intelligent Technique ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Incremental Step

PurposeThis paper aims to generate an obstacle free real time optimal path in a cluttered environment for a two-wheeled mobile robot (TWMR).Design/methodology/approachThis TWMR resembles an inverted pendulum having an intermediate body mounted on a robotic mobile platform with two wheels driven by two DC motors separately. In this article, a novel motion planning strategy named as DAYANI arc contour intelligent technique has been proposed for navigation of the two-wheeled self-balancing robot in a global environment populated by obstacles. The developed new path planning algorithm evaluates the best next feasible point of motion considering five weight functions from an arc contour depending upon five separate navigational parameters.FindingsAuthenticity of the proposed navigational algorithm has been demonstrated by computing the path length and time taken through a series of simulations and experimental verifications and the average percentage of error is found to be about 6%.Practical implicationsThis robot dynamically stabilizes itself with taller configuration, can spin on the spot and rove along through obstacles with smaller footprints. This diversifies its areas of application to both indoor and outdoor environments especially with very narrow spaces, sharp turns and inclined surfaces where its multi-wheel counterparts feel difficult to perform.Originality/valueA new obstacle avoidance and path planning algorithm through incremental step advancement by evaluating the best next feasible point of motion has been established and verified through both experiment and simulation.

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