Obstacle Avoidance Method for Mobile Robots Based on the Identification of Local Environment Shape Features

Aiming at the problem that algebraic models used in sensor information analysis are prone to simplify or even ignore environmental details,we propose a novel laser data processing method based on multiple expansions for mobile robot obstacle avoidance. By expanding obstacle edges with several ratios and analyzing the characteristics of expansion maps,the proposed method extracts shape features of the local environment to enhance robots' ability inlocal environmental recognition and obstacle avoidance. The flexibility of online selection and the switch of the expansion ratios facilitates robots that are adaptableto environments of various shapes and sizes. In robot motion control,environmental shape features are also introduced for motion decision-making,while collision avoidance is added to ensure the security of robots in narrow environments. Simulations verify that the proposed method is competent for robots' motion planning in different environments,and a real platform experiment validates the method's feasibility for practical applications with low-precision control signals,response delay,and tire slippage.