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Path planning of unmanned aerial vehicle based on improved gravitational search algorithm

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Abstract

Path planning of Uninhabited Aerial Vehicle (UAV) is a complicated global optimum problem. In the paper, an improved Gravitational Search Algorithm (GSA) was proposed to solve the path planning problem. Gravitational Search Algorithm (GSA) is a newly presented under the inspiration of the Newtonian gravity, and it is easy to fall local best. On the basis of introducing the idea of memory and social information of Particle Swarm Optimization (PSO), a novel moving strategy in the searching space was designed, which can improve the quality of the optimal solution. Subsequently, a weighted value was assigned to inertia mass of every agent in each iteration process to accelerate the convergence speed of the search. Particle position was updated according to the selection rules of survival of the fittest. In this way, the population is always moving in the direction of the optimal solution. The feasibility and effectiveness of our improved GSA approach was verified by comparative experimental results with PSO, basic GSA and two other GSA models.

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Authors and Affiliations

  1. Science and Technology on Aircraft Control Laboratory, School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Pei Li & HaiBin Duan

  2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    HaiBin Duan

Authors
  1. Pei Li
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  2. HaiBin Duan
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Correspondence to HaiBin Duan.

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Li, P., Duan, H. Path planning of unmanned aerial vehicle based on improved gravitational search algorithm. Sci. China Technol. Sci. 55, 2712–2719 (2012). https://doi.org/10.1007/s11431-012-4890-x

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  • DOI: https://doi.org/10.1007/s11431-012-4890-x

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