Abstract
Path planning is the focus and difficulty of research in the field of mobile robots, and it is the basis for further research and applications of robots. In order to obtain the global optimal path of the mobile robot, an improved moth-flame optimization (IMFO) algorithm is proposed in this paper. The IMFO features the following two improvement. Firstly, referring to the spotted hyena optimization (SHO) algorithm, the concept of historical best flame average is introduced to improve the moth-flame optimization (MFO) algorithm update law to increase the ability of the algorithm to jump out of the local optimum; Secondly, the quasi-opposition-based learning (QOBL) is used to perturb the location, increase the population diversity and improve the convergence rate of the algorithm. In order to evaluate the performance of the proposed algorithm, the IMFO algorithm is compared with three existing algorithms on three groups of different types of benchmark functions. The comparative results show that the IMFO algorithm is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Finally, the IMFO algorithm is applied to the path planning of the mobile robot, and computer simulations confirmed the algorithm’s effectiveness.
Highlights
Path planning technology is one of the core elements of research in the field of mobile robotics, and its purpose is to generate an optimal or near-optimal collision-free path from the starting point to the end point for a mobile robot
Have emerged in reference [1], an improved path initialization method is proposed to address the fact that the path initialization process can affect the performance of genetic algorithms, and simulation experiments show that the method can obtain high-quality paths in a shorter period of time
The reference [2] addressed the shortcomings of the artificial potential field method in path planning, which is prone to fall into local optimum, and improved the repulsive function and combined it with a fuzzy inference strategy to better achieve multi-robot path planning
Summary
Path planning technology is one of the core elements of research in the field of mobile robotics, and its purpose is to generate an optimal or near-optimal collision-free path from the starting point to the end point for a mobile robot. The reference [2] addressed the shortcomings of the artificial potential field method in path planning, which is prone to fall into local optimum, and improved the repulsive function and combined it with a fuzzy inference strategy to better achieve multi-robot path planning. The swarm intelligence algorithm has a good performance, it has problems such as slow convergence speed and easy to fall into local optimal solutions. The reference [10] combined the particle swarm algorithm (PSOA) with the gravitational search algorithm (GSA) algorithm to balance the global and local search capabilities of particles These improved algorithms have faster convergence, higher accuracy and better performance compared to the standard algorithm for path planning applications. In order to better apply the MFO algorithm to mobile robot path planning, this paper proposed an improved moth-flame optimization algorithm.
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