Dynamic obstacles avoidance for autonomous mobile robots based on kinematic constraints and IAPF methodology

Abstract

Aiming at the degraded performance of artificial potential field (APF) technology when facing dynamic obstacles in modern warehouse scenarios, this paper proposes an improved artificial potential field (IAPF) methodology, which can effectively solve problems such as target unreachability and local minimum. First, a redefined repulsive field function is designed to decline the repulsive force working on the autonomous mobile robot (AMR) when the target point is within the influence radius of an obstacle, which can solve the target unreachability problem successfully. Second, a new sub-target selection method based on obstacle risk assessment value is adopted by IAPF, which can select more scientific sub-target points with better extrication effects compared to the traditional selection method based on the density of obstacles. Then, a kinematic constraint incorporating proportional control and a hierarchical deceleration strategy are proposed to enhance path feasibility and reduce oscillations. Moreover, three scenarios (structured, semi-structured, and unstructured) in warehousing environments are built up in a simulation platform to validate the effectiveness of IAPF, and the results verify its feasibility and stability for dynamic obstacle avoidance.