Dynamic Obstacle Avoidance for Mobile Robots Based on 2D Differential Euclidean Signed Distance Field Maps in Park Environment

Abstract

In this paper, a novel and complete navigation system is proposed for mobile robots in a park environment, which can achieve safe and stable navigation as well as robust dynamic obstacle avoidance. The navigation system includes a global planning layer and a local planning layer. The global planner plans a series of way-points using the A* algorithm based on an offline stored occupancy grid map and sends them to the local planner. The local planner incorporates a dynamic obstacle avoidance mechanism. In contrast to existing dynamic obstacle avoidance algorithms based on trajectory tracking, we innovatively construct a two-dimensional Difference ESDF (Euclidean Signed Distance Field) map to represent obstacle motion information. The local planner outputs control actions by scoring candidate paths. A series of simulation experiments and real-world tests are conducted to verify that the navigation system can safely and robustly accomplish navigation tasks. The safety distance of the simulation experiment group with the dynamic obstacle avoidance scoring item added increased by 1.223 compared to the group without the dynamic obstacle avoidance scoring item.