The Comparison and Analysis of Autonomous Food Delivery Robot Based on Artificial Potential Field and Breadth-First Search Methods

The autonomous food delivery robot, as one of the most popular products of Artificial Inteligence, is to plan the path accurately from the chef to the customer’s table while avoiding unnecessary collisions with the obstacles like tables, chairs and pedestrians. This paper makes a comparative analysis of the path planning technology in the process of robot food delivery in the restaurant environment. Firstly, the restaurant environment is analyzed and simulated in Webots. Then, according to the environmental requirements, the effects of Artificial Potential Field(APF), improved APF and Breadth- First Search (BFS) on path planning and obstacle avoidance are compared and analyzed respectively. Subsequently, the whole scene is simulated in Webots and the comparison is generated based on the operation time, traveling distance, completeness, and the smoothness of the robot’s motion. Finally, by comparing the experimental results, the advantages and disadvantages of each algorithm are analyzed. It is concluded that in small-scale restaurant environment, BFS algorithm will achieve better results in route planning.