An Optimization Path Planning Method Based on Evolutionary Artificial Potential Field

Li, Fei Long

doi:10.4028/www.scientific.net/amm.380-384.1414

Cited by 1 publication

(1 citation statement)

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“…[1]. e traditional algorithms to solve the path planning problem mainly include artificial potential field algorithm [2], Dijkstra algorithm [3], and A * algorithm [4]. In recent years, some researchers have adopted bionic intelligent optimization algorithms to solve the problem of path planning.…”

Section: Introductionmentioning

confidence: 99%

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Wang

Zhao

et al. 2021

Journal of Robotics

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Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

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Section: Introductionmentioning

confidence: 99%