Research on Artificial Bee Colony Method Based Complete Coverage Path Planning Algorithm for Search and Rescue Robot

Abstract

In terms of the low efficiency of search and rescue robot's complete coverage path planning in the complex working environment, based on the traditional neural network optimization strategy, a complete coverage path planning strategy based on Artificial Bee Colony algorithm is proposed. Using the data collected by lidar or other sensors to build an environmental map model, with search and rescue robot as the main body, a neural network forward propagation model combined with an artificial bee colony algorithm was established, and the artificial bee colony method was used to optimize the parameters of the neural network by unsupervised training. It has improved the operability and portability of search and rescue robot working in different environments. And on this basis, an evaluation equation for evaluating the effect of complete coverage path planning is proposed, so as to automatically evaluate the quality of the generated complete coverage path during training. The simulation results show that for the unknown environment, based on the improved path planning algorithm, it has higher efficiency when performing the complete coverage path planning mission of the search and rescue robot.