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

摘要

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.