Safe Interaction of Automated Forklifts and Humans at Blind Corners in a Warehouse with Infrastructure Sensors

Abstract

Co-working and interaction of automated systems and humans in a warehouse is a significant challenge of progressing industrial systems' autonomy. Especially, blind corners pose a critical scenario, in which infrastructure-based sensors can provide more safety. The automation of vehicles is usually tied to an argument on improved safety. However, current standards still rely on the awareness of humans to avoid collisions, which is limited at corners with occlusion. Based on the examination of blind corner scenarios in a warehouse, we derive the relevant critical situations. We propose an architecture that uses infrastructure sensors to prevent human-robot collisions at blind corners with respect to automated forklifts. This includes a safety critical function using wireless communication, which sporadically might be unavailable or disturbed. Therefore, the proposed architecture is able to mitigate these faults and gracefully degrades performance if required. Within our extensive evaluation, we use a warehouse simulation to verify our approach and to estimate the impact on an automated forklift's performance.

Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments

“…Sama et al [32] present a reinforcement learning algorithm trained on human data to determine how the intelligent vehicle should behave, whereas Solomitckii et al [33] propose the use of mmWave radar with reflectors installed at the junction to allow the intelligent vehicle to see around corners. Other works take into consideration the use of external sensors via V2I communications to inform the motion planning of the intelligent vehicle [34,35]. Fewer works address the communications perspective of this occluded use case.…”

Experimental Evaluation and Modeling of the Accuracy of Real-Time Locating Systems for Industrial Use

“…Consequently, this can result in unnecessary delays, interruptions, or the risk of getting stuck in deadlocks. By leveraging real-time localization data, RTLSs empower AMRs to accurately perceive their surroundings in a timely manner to adapt their paths accordingly, ensuring smooth and efficient navigation [3], [4].…”