TSU · Nashville, TN

Tennessee State University

Physics-based simulation of urban air mobility environments and resilient emergency landing for eVTOL aircraft and drones.

IRIS simulationSafe landing zonesMultimodal sensingSynthetic datasets

Tennessee State University’s team works in the Intelligent Tactical Mobility Robotics Laboratory, directed by Amir Shirkhodaie. The team develops IRIS, a physics-based simulation environment for urban air mobility and drone delivery, and uses it to study autonomous selection of emergency safe landing zones when an aircraft faces sensing failures or cyberattacks, combining computer vision, multi-sensor fusion, and hypothesis-driven decision making. It also generates annotated synthetic datasets for drone delivery, collaborates with GWU and UC Irvine, and runs outreach programs for students.

Research thrusts

  1. 01

    IRIS simulation environment

    Physics-based downtown, suburban, vertiport, and airport scenes with eVTOL and drone models and simulated cameras, LiDAR, GPS, and inertial sensors under degraded weather and lighting.

    UC-06

  2. 02

    Emergency safe landing zones

    A multi-hypothesis framework that generates, validates, ranks, and tracks landing candidates using imagery and LiDAR, published at SPIE Defense + Security 2026.

    UC-06

  3. 03

    IRIS D³ drone-delivery dataset

    Dozens of delivery scenarios and more than 20,000 synthetic RGB images with matching LiDAR point clouds, from clean pads to intrusions and degraded visibility.

    UC-01with GWUwith UC Irvine

Figures

From the research

  • Simulated drone-delivery scene: Clean padClean pad
  • Simulated drone-delivery scene: Degraded padDegraded pad
  • Simulated drone-delivery scene: Multiple padsMultiple pads
  • Simulated drone-delivery scene: Invalid drop zoneInvalid drop zone
  • Simulated drone-delivery scene: Patio / balconyPatio / balcony
  • Simulated drone-delivery scene: IntrusionIntrusion
  • Simulated drone-delivery scene: Shadow / occlusionShadow / occlusion
  • Simulated drone-delivery scene: Degraded visibilityDegraded visibility
TSU Representative scenarios from the IRIS D³ multimodal drone-delivery dataset, rendered in RGB with matching simulated LiDAR.
3D city model with many curved magenta flight paths between buildings.
TSUMulti-eVTOL routing between vertiports in a simulated downtown.
Point-cloud rendering of city buildings colored by height.
TSUSimulated LiDAR imagery of a physics-based downtown model.
Rendered rooftop vertiport with an eVTOL aircraft on a landing pad.
TSUA vertiport modeled in TSU’s IRIS physics-based simulation environment.
Block diagram of the emergency landing framework from perceptual inputs through validation to flight-control handoff.
TSUTSU’s multimodal emergency safe-landing-zone framework, from sensing and candidate generation through semantic and geometric validation, tracking, and human-in-the-loop review.
Synthetic aerial view beside a LiDAR-derived occupancy map with ranked landing candidates.
TSUCandidate landing zones checked against simulated LiDAR terrain for slope, roughness, and obstacle clearance.
Aerial neighborhood image beside the same image annotated with detected buildings, roads, trees, and candidate landing circles.
TSUElectro-optical semantic landing-zone selection combining classical image processing with a learned model.

Demos

See TSU's work in action

All demos

TSUUC-01Simulation

Simulated package delivery

A drone descends to a residential drop-off in TSU’s IRIS simulator, one of the scenarios behind the IRIS D³ drone-delivery dataset.

TSUUC-06Simulation

IRIS: multi-UAM flight simulation

A tour of TSU’s IRIS physics-based simulation, with downtown, suburban, and airport scenes and several eVTOL aircraft flying coordinated routes.

TSUUC-06Simulation

One simulator, many sensors and conditions

IRIS scenes rendered as RGB, thermal-infrared, night-vision, and LiDAR imagery, and under fog and snow. These are the conditions our perception methods are tested in.

TSUUC-06Simulation

Finding landing-zone candidates

Candidate safe landing zones are detected in aerial imagery and compared by size and clearance before one is selected.

TSUUC-06Simulation

Tracking a safe landing zone during descent

TSU’s emergency landing system locks onto the selected landing zone and keeps it centered as the aircraft descends, with live tracking plots.

Use cases

Where TSU contributes

People

Researchers and students

Postdoctoral researchers1

  • Branndon Jones

PhD students1

  • Mohammad Amin Hodaei

MS students3

  • Nischal Aryal
  • Kane Miller
  • Abdul Kareem Ras

Undergraduate researchers1

  • James McWilliams

Former members

  • Amer IbrahimMS student

Publications

Publications

2 published or accepted papers

  1. Emergency Safe Landing Zone Selection in Unmanned Aerial Systems

    N. Aryal, B. Jones, A. Shirkhodaie

    Proc. SPIE 14047, Unmanned Systems Technology XXVIII, 2026

    ConferenceTSUUC-06

  2. Dynamic Urban Air Mobility Systems Modeling and Simulation

    A. M. Ibrahim

    MS thesis, Tennessee State University, 2025

    ThesisTSUUC-06