Abstract
Swarm intelligence algorithms have better intelligence and adaptation compared with the traditional route planning method. A three-dimensional route planning method based on the beetle swarm optimization (BSO) algorithm was proposed. The iterative updating strategy of the BSO algorithm cooperated with the search mechanism of the beetle monomer and the updating strategy of the particle swarm optimization (PSO) algorithm, thus accelerating iterative convergence and decreasing the probability of trapping in the local optimal solution of the algorithm. The practical engineering problem of three-dimensional route planning was addressed by processing uneven ground barriers using the penalty function, and a smooth route is gained from cubic spline interpolation. In this study, a three-dimensional environmental model was constructed by using actual elevation data from the USGS/NASA SRTM, and a simulation experiment of three-dimensional route planning was performed using the proposed method. The proposed method was compared with other algorithms. Experimental results demonstrated that when the iteration time was set to 50, the route planning length based on BSO algorithm was about 90% of the route planning based on the PSO algorithm. Moreover, the proposed route planning method had high convergence rate and stable convergence result and is applicable to three-dimensional route planning.
Highlights
Route planning is one of key technologies that realize intelligent control and autonomous cruising of robots
Experimental results demonstrated that when the iteration time was set to 50, the route length, which was planned by beetle swarm optimization (BSO) algorithm, was about 90% of that planned based on the particle swarm optimization (PSO) algorithm
The proposed three-dimensional route planning method based on BSO algorithm has the following innovations: 1) In route planning based on the PSO algorithm, a particle must generally run through the coordinates of all discrete points on a route, determining the planned route
Summary
Route planning is one of key technologies that realize intelligent control and autonomous cruising of robots. Swarm intelligence algorithms have self-organization, self-learning functions, some fault-tolerance capability, and significantly high optimization efficiency When these algorithms are applied to three-dimensional route planning, they can improve robot flexibility and intelligence in a nonstructured environment. In [9], a membrane evolutionary artificial potential field approach was proposed to solve the mobile robot route planning problem, which combines membrane computing with a genetic algorithm and the artificial potential field method to find the parameters to generate a feasible and safe route. Mao and Pang [16] planned routes in a complicated ocean environment using an underwater robot based on the PSO algorithm and improved the parameter control strategy and topological model.
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