industrial logistics teams

Abstract: This paper evaluates an archival Bluetooth Low Energy received signal strength indicator (RSSI) dataset collected for bin localization on an industrial shop floor. The installation used 24 fixed relays positioned on walls and Bluetooth beacons attached to bins, with each relay applying a Kalman filter to its own readings in firmware before publishing them. The primary survey produced 19,034 usable two-minute fingerprint samples across 90 known positions, with 23 relay measurements available as model features. We compare calibrated path-loss multilateration with two radio-map estimators: distance-weighted $k$-nearest neighbours and Random Forest regression. A random 70/30 row split measures recognition at surveyed positions, while a coordinate-grouped split reserves 27 entire positions for testing and measures interpolation to unseen positions. Random Forest achieved a median error of 0.66~m for surveyed positions, with 77.9% of estimates within 3~m and 96.3% within 10~m. For unseen positions, median error increased to 9.42~m and 55.5% of estimates were within 10~m. Path-loss multilateration produced a 19.41~m median error on unseen positions, close to the 19.62~m mean-position baseline. The results support RSSI fingerprinting for a fixed inventory of surveyed bin positions, but not precise continuous localization between survey points. Git history dates the original implementation to September 7, 2019. The evaluation code in this paper was written in October 2026 and is reported separately from the seven-year-old prototype.

Abstract: Automated Guided Vehicle (AGV) fleets in production and logistics environments rely on a system-wide emergency stop to handle local anomalies such as malfunctions, hazards, or contaminated areas. While safe, this approach halts the entire fleet even when only a small area is affected, causing unnecessary downtime. This paper proposes dead zones --- a formally defined localized isolation approach that serves as an additional fail-operational mechanism for situations where a full system-wide halt is not strictly needed. Rather than shutting down the entire fleet, a dead zone isolates the anomalous area, allowing unaffected robots to continue operating normally. We introduce two operational strategies: basic dead zone handling, in which affected robots enter a dead zone and stop, and dead zone handling with escaping, in which robots that are sufficiently far from a dead zone reroute around it. For escaping on counterclockwise trajectories we prove collision-freedom with a bounded arrival delay for all escaping robots, provided a minimum pairwise separation is kept between robots. Experimental evaluation confirms the theoretical guarantees and characterizes the throughput tradeoffs between all three approaches: full emergency stop, basic dead zone handling, and dead zone handling with escaping.

Abstract: Multiple UAVs can cooperatively transport heavy payloads while controlling their position and orientation. Trajectory-based methods offer high agility while satisfying system constraints, but can produce uneven force distributions when the tension-to-wrench allocation is redundant or ill-conditioned, particularly under geometric mismatch and low-level tracking errors. We propose a hybrid planning-and-control framework to address this problem. A global planner generates payload trajectories and cable-force references by exploring the allocation null space under a prescribed internal-force setting. These references augment the cost of a centralized local planner, promoting feasible force distributions while generating trajectories for all UAVs. An admittance filter then compares the planned forces with onboard cable-tension estimates and adjusts the kinematic references to improve force tracking in degenerate or near-degenerate configurations. Simulations and experiments involving four to ten UAVs demonstrate more balanced tension distributions during both hovering and demanding agile maneuvers, without compromising agility or payload-tracking performance.