warehouse operations managers

Abstract: In smart factory environments, passive entities such as humans and movable obstacles may interfere with autonomous robot operations, posing significant safety risks. Detecting potential conflicts between robots and entities outside their field-of-view (FoV) is therefore of paramount importance. However, conventional passive object detection systems rely on sensors such as LiDAR, radar, or cameras, which operate under direct line-of-sight (LoS) and are limited by obstacles and occlusions. To overcome these limitations, integrated sensing and communication (ISAC) enables environmental perception via the communication infrastructure thanks to cooperation among distributed wireless devices. In this paper, we propose a framework for passive entity sensing beyond the robot's FoV that relies solely on distributed channel measurements from the communication infrastructure to perform risk-aware classification. These measurements are embedded into high-dimensional representations using hyperdimensional computing (HDC), enabling robust and scalable learning without explicit channel reconstruction. Numerical simulations based on a commercial ray tracer and ROS-based validation in realistic smart factory scenarios demonstrate improved safety and enhanced path planning with up to 89% accuracy under severe occlusions, while maintaining low computational complexity.

Abstract: This study compares two implemented intra-aisle robotic-picking configurations through discrete-event simulation. Without an onboard tote lift, two level-captive shuttles serve the two tested levels; with the lift, one shuttle remains on its horizontal level and its onboard mechanism moves the order tote to allow picks from the adjacent upper level. Thirty configuration-task-size experiments (n = 1-15 locations; ten replications each) yield 300 replication-level cycle-time observations. Two-sided Welch tests are applied to the replication means, with Holm adjustment across the 15 comparisons. The onboard-tote-lift configuration has longer mean cycle time at all tested task sizes. At n = 15, the means are 299.477 and 333.231 s/cycle, respectively: a 33.753 s increase (11.27%). The corresponding cycle-time-derived item rates are 180.314 and 162.050 items/h (-10.13%). These rates are calculated as 3600n divided by the scenario-mean cycle time; they are not measurements of the combined concurrent capacity of two level-captive shuttles. Importantly, task locations are sampled from different eligible-level sets in the two configurations, so the result concerns the implemented configuration-and-task-generation packages, not the isolated effect of a five-second tote-lift operation under identical picked locations. The study quantifies a cycle-performance difference alongside a reduction from two dedicated shuttles to one within the tested two-level block; it does not quantify system-level maximum throughput, investment cost, or reliability.

Abstract: Automation systems must adapt to changing tasks, equipment states, and staffing conditions while providing evidence for human review. This study presents a multi-line task-adjustment system integrating a local large language model, a digital twin, and human decision-making. A Propose-Verify-Decide workflow translates operator intent into structured requirements, generates a bounded set of candidate strategies, and checks semantics, simulation execution, and operational constraints. Linked records preserve traceability from requests to verification evidence and decisions. Thirty fixed test records were evaluated using four virtual surgical-instrument sorting lines: 28 assessed the workflow and two assessed model generation. Eighteen workflow cases met expectations; autonomous strategy-workflow success was 3/10, and correct rejection of invalid inputs was 7/8. All four cases that passed preceding checks, produced complete evidence, and reached final engineering review (CP6) passed that review. Together with the correct blocking of strategies that failed throughput constraints, this supports the effectiveness of staged screening and confirmation within the tested setting. Mean placement-validation pass rate across eight simulation evidence records was 97.50%. Mean times to the first reviewable response and simulation verification, excluding startup, were 12.94 and 164.39 s, respectively. Remaining failures involved semantic distortion, incomplete evidence, and missed invalid inputs. The results demonstrate a traceable strategy-review workflow, but do not establish overall reliability or long-term stability. Broader testing and physical evaluation are needed to assess generalizability.

Abstract: The coordination of Automated Guided Vehicles (AGVs) in high-density industrial environments represents a critical challenge within Logistics 4.0, as traditional traffic management methods often lead to inefficiencies caused by negotiation-based priority assignment. To overcome the resulting limitations, this paper presents an innovative AGV traffic management system based on a Lifelong Multi-Agent Path Finding (L-MAPF) algorithm operating on roadmaps generated with Non-Uniform Rational B-Splines (NURBS) curves. The approach guarantees locally optimal coordination and ensures safe operation of large and heterogeneous AGVs. Building on this concept, the proposed framework integrates a modified version of the Bounded Horizon Conflict Based Search (CBS) technique within a Rolling Horizon Conflict Resolution strategy, utilizing an extended time horizon for each agent to enable effective conflict resolution in corridors identified by a topological map. In contrast to state-of-the-art methods for AGV fleet traffic management, the proposed solution is designed for real-world, non-standardized (i.e., non-grid-like) industrial settings characterized by narrow bidirectional corridors and high-traffic density, where AGVs of various sizes and capabilities operate simultaneously. Key contributions include an anytime conflict resolution strategy with adaptive time horizon regulation, an execution layer for safe and standard-compliant interaction with real AGVs, and an advanced mechanism for deadlock detection and resolution. Experimental results obtained in realistic industrial environments demonstrate higher throughput, with improvements of up to 11% over a conventional rule-based traffic management system, a state-of-the-art industrial method, and a priority-based L-MAPF variant, while maintaining continuous operation and improved efficiency.