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 proposedMGWOhas better optimization performance. When combined with a situation assessment mechanism, it
realizes dynamic obstacle avoidance while keeping the path length as short as possible.
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