Search

So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of $23.5\;\text{s}$ at a success rate of $94.7\%$ . Our system is able to autonomously carry out simple order picking tasks in a human-safe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.

We address the problem of human detection from heavy mobile machinery and robotic equipment operating at industrial working sites. Exploiting the fact that workers are typically obliged to wear high-visibility clothing with reflective markers, we propose a new recognition algorithm that specifically incorporates the highly discriminative features of the safety garments in the detection process. Termed Multi-band Hough Forest, our detector fuses the input from active near-infrared (NIR) and RGB color vision to learn a human appearance model that not only allows us to detect and localize industrial workers, but also to estimate their body orientation. We further propose an efficient pipeline for automated generation of training data with high-quality body part annotations that are used in training to increase detector performance. We report a thorough experimental evaluation on challenging image sequences from a real-world production environment, where persons appear in a variety of upright and non-upright body positions.

We propose and evaluate a system for detecting and tracking multiple humans wearing high-visibility clothing from vehicles operating in industrial work environments. We use a customized stereo camera setup equipped with IR flash and IR filter to detect the reflective material on the worker's garments and estimate their trajectories in 3D space. An evaluation in two distinct industrial environments with different degrees of complexity demonstrates the approach to be robust and accurate for tracking workers in arbitrary body poses, under occlusion, and under a wide range of different illumination settings.

We report on a novel vision-based method for reliable human detection from vehicles operating in industrial environments in the vicinity of workers. By exploiting the fact that reflective vests represent a standard safety equipment on most industrial worksites, we use a single camera system and active IR illumination to detect humans by identifying the reflective vest markers. Adopting a sparse feature based approach, we classify vest markers against other reflective material and perform supervised learning of the object distance based on local image descriptors. The integration of the resulting per-feature 3D position estimates in a particle filter finally allows to perform human tracking in conditions ranging from broad daylight to complete darkness.