Scenario 19 reproduces the trajectory of a line-following robot, inspired by a real-world experiment conducted with MikroTik radios. The robot model was simplified for simulation purposes, while preserving the proportions and dimensions of the physical prototype, enabling a consistent representation of its motion and antenna position along the path. The LASSE meeting room was used as the indoor simulation environment, including multiple objects and structural elements present in the real setting to improve geometric and propagation fidelity. The robot mobility was implemented in SUMO through an equivalent pedestrian route, ensuring a controlled and repeatable trajectory. This dataset was created primarily to (i) replicate the real experiment as faithfully as possible and (ii) validate the use of a new robot-specific receiver template within the Raymobtime tool.


A LASSE Meeting Room scenario models a realistic indoor meeting environment based on the actual LASSE conference room. The geometry was recreated in Blender with accurate, real-world dimensions, and includes multiple elements present in the real space (e.g., walls, doors/glass partitions, a large central meeting table, surrounding desks, chairs, and other furniture). This dense and faithful object placement was intentionally adopted to keep the simulated environment as close as possible to the real site, providing rich reflecting and scattering surfaces and creating frequent occlusion opportunities typical of indoor deployments—especially around the central table area.

Robot mobility is defined in SUMO using a pedestrian-style route that emulates the motion of a line-following robot. The robot traverses a closed path around the meeting table, with a total route length of 15.43 m, modeled as a single-lane indoor path (number of lanes: 1) with 0.5 m effective width and an average speed of 1.34 m/s. As the robot circulates the table, the link conditions naturally vary due to furniture-induced blockages and cornering, making the scenario well-suited for evaluating indoor propagation effects (multipath, partial/complete blockage) and beam-management behavior under controlled, repeatable motion in a high-fidelity room model.


The collected data was generated using the Raymobtime pipeline, which couples vehicular and ´pedestrian traffic simulation in SUMO with scene generation and ray-tracing in Remcom Wireless InSite (WI) via a Python orchestrator.

1. Ray tracing data: top 100 rays of highest received power, obtained from Wireless Insite (WI).

  • Received power, time of arrival, elevation angle of departure, azimuth angle of departure, elevation angle of arrival, azimuth angle of arrival, LOS condition.
  • Type data: .hdf5 of each episode
  • At database .db file is possible take rays paths and interactions information.

Raymobtime Scenario 14 – V2I Rosslyn 60 GHz