Work

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

Year: 2022

Type: article

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.

Source: Soft Computing

DOI: 10.1007/s00500-022-07075-y

Authors: Xiaodong Na, Jiaqian Wang, Min Han, Decai Li

Institutions: Dalian University of Technology, Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: State Key Laboratory of Robotics, Fundamental Research Funds for the Central Universities

Awards: G6356639868, G6735171862, G6955742492