scholarly journals Gradient Eigen-decomposition Invariance Biogeography-based Optimization for Mobile Robot Path Planning

2021 ◽  
Author(s):  
Jiaqian Wang ◽  
Xiaodong Na ◽  
Min Han ◽  
Deicai Li
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Fitness Function ◽  
Rotation Invariance ◽  
Search Performance ◽  
Robot Path Planning ◽  
Series Of Experiments ◽  
Eigen Solutions ◽  
Eigen Decomposition ◽  
Robot Path

Abstract The path planning for mobile robots has attracted extensive attention, and evolutionary algorithms have been applied to this problem increas-ingly. In this paper, we propose a novel gradient eigen-decomposition invariance biogeography-based optimization (GEI-BBO) for mobile robot path planning, which has the merits of high rotation invariance and excel-lent search performance. In GEI-BBO, we design an eigen-decomposition mechanism for migration operation, which can reduce the dependence of biogeography-based optimization (BBO) on the coordinate system, improve the rotation invariance and share the information between eigen solutions more effectively. Meanwhile, to find the local opti-mal solution better, gradient descent is added, and the system search strategy can reduce the occurrence of local trapping phenomenon. In addition, combining the GEI-BBO with cubic spline interpola-tion will solve the problem of mobile robot path planning through a defined coding method and fitness function. A series of experiments are implemented on benchmark functions, whose results indicated that the optimization performance of GEI-BBO is superior to other algo-rithms. And the successful application of GEI-BBO for path planning in different environments confirms its effectiveness and practicability.

Path Planning in a Mobile Robot

2019 ◽  
pp. 582-608
Author(s):  
Diego Alexander Tibaduiza Burgos ◽  
Maribel Anaya Vejar
Keyword(s):  
Genetic Algorithms ◽  
Path Planning ◽  
Mobile Robot ◽  
Fitness Function ◽  
Experimental Setup ◽  
Robot Path Planning ◽  
Natural Evolution ◽  
On Line ◽  
Planning Problems ◽  
Robot Path

This chapter presents the development and implementation of three approaches that contribute to solving the mobile robot path planning problems in dynamic and static environments. The algorithms include some items regarding the implementation of on-line and off-line situations in an environment with static and mobile obstacles. A first technique involves the use of genetic algorithms where a fitness function and the emulation of the natural evolution are used to find a free-collision path. The second and third techniques consider the use of potential fields for path planning using two different ways. Brief descriptions of the techniques and experimental setup used to test the algorithms are also included. Finally, the results applying the algorithms using different obstacle configurations are presented and discussed.

Path Planning in a Mobile Robot

2013 ◽  
pp. 123-147
Author(s):  
Diego Alexander Tibaduiza Burgos ◽  
Maribel Anaya Vejar
Keyword(s):  
Genetic Algorithms ◽  
Path Planning ◽  
Mobile Robot ◽  
Fitness Function ◽  
Experimental Setup ◽  
Robot Path Planning ◽  
Natural Evolution ◽  
On Line ◽  
Planning Problems ◽  
Robot Path

This chapter presents the development and implementation of three approaches that contribute to solving the mobile robot path planning problems in dynamic and static environments. The algorithms include some items regarding the implementation of on-line and off-line situations in an environment with static and mobile obstacles. A first technique involves the use of genetic algorithms where a fitness function and the emulation of the natural evolution are used to find a free-collision path. The second and third techniques consider the use of potential fields for path planning using two different ways. Brief descriptions of the techniques and experimental setup used to test the algorithms are also included. Finally, the results applying the algorithms using different obstacle configurations are presented and discussed.

scholarly journals Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110192
Author(s):  
Songcan Zhang ◽  
Jiexin Pu ◽  
Yanna Si ◽  
Lifan Sun
Keyword(s):  
Path Planning ◽  
Convergence Rate ◽  
Mobile Robot ◽  
Local Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Geometric Features ◽  
Robot Path Planning ◽  
Computation Efficiency ◽  
Robot Path

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.

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