Gradient eigendecomposition invariance biogeography-based optimization for mobile robot path planning

Abstract

The path planning for mobile robots has attracted extensive attention, and evolutionary algorithms have been applied to this problem increasingly. In this paper, we propose a novel gradient eigendecomposition invariance biogeography-based optimization (GEI-BBO) for mobile robot path planning, which has the merits of high rotation invariance and excellent search performance. In GEI-BBO, we design an eigendecomposition mechanism for migration operation, which can reduce the dependency of biogeography-based optimization (BBO) on the coordinate system, improve the rotation invariance and share the information between eigensolutions more effectively. Meanwhile, to find the local optimal 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 interpolation 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 algorithms. And the successful application of GEI-BBO for path planning in different environments confirms its effectiveness and practicability.