Abstract
To better solve the problems associated with optimal pathfinding and dynamic obstacle avoidance in the path planning of mobile robots, a hybrid path planning scheme combining modified gray wolf optimization (MGWO) and situation assessment mechanism is proposed. Firstly, a MGWO algorithm is proposed to plan a global path. Secondly, different situational factors for robots in different regions are extracted from the fusion results of 2D laser measurements and image data, and a Bayesian network model of robot action selection is established. Then, the situational factors of the robot are used as evidence for reasoning. Based on the posterior probability value in the inference result, the grid to be moved is selected and the traveling direction of the robot is adjusted in order to take advantage of both global path planning and local dynamic obstacle avoidance. The simulation results show that the proposed MGWO has better optimization performance. When combined with a situation assessment mechanism, it realizes dynamic obstacle avoidance while keeping the path length as short as possible.
Highlights
With technological advancement and social development, the level of intelligence and automation of mobile robots has gradually improved, and it has gradually penetrated into people’s daily life [1]
Is article proposes a mobile robot path planning scheme based on the modified gray wolf optimization (MGWO) and situation assessment mechanism
E contributions of this article are listed as follows: (1) To solve the problem of global path planning of the mobile robot, a modified gray wolf optimization algorithm (MGWO) is proposed in which the population diversity is enhanced by logistic chaotic mapping
Summary
With technological advancement and social development, the level of intelligence and automation of mobile robots has gradually improved, and it has gradually penetrated into people’s daily life [1]. E mobile robot needs to plan a short, energy-efficient, and safe path from the initial position to the target position, and it must be able to avoid static and dynamic obstacles along the way. Is article proposes a mobile robot path planning scheme based on the MGWO and situation assessment mechanism. (1) To solve the problem of global path planning of the mobile robot, a modified gray wolf optimization algorithm (MGWO) is proposed in which the population diversity is enhanced by logistic chaotic mapping. E initial path based on global environment information is obtained with the MGWO algorithm. When an obstacle is detected, the situation assessment mechanism is used to avoid the obstacle through local planning and back to the global path.
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