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UC04.3: Detailed indoor exploration with ANYmal (USAR)

Version 11.1 by Tjalling Haije on 2026/02/16 08:19
ObjectiveOB01: Increase effectiveness through exploration, sensing and communication means
OB04: Improve safety by providing overviews and warnings of environment conditions
Description

Enable safe, data‑driven interior reconnaissance and victim triage before human entry, with continuous FR health/location monitoring informing decisions, rotations, and evacuation.

TDP1a: Pattern: “Robots first, then humans.”, 2c: IDP "role-based, context-specific views", 
IDP 
ActorsTeam leader, Robot operator, Analyst, Safety officer (at HQ), Entry Team (FRs) (described at 4. Personas & Problem Scenarios and Direct Stakeholders)
Pre-Condition
  • Structure is flagged as unsafe for direct human entry; sector/ worksite allocated.
  • ANYmal is mission-ready (battery, controls, and payload checked).
  • FRs wearables distributed, connected, and calibrated.
  • C3I is online, role-based views are configurated (Operator/ Analyst/ Team leader/ Safety officer); 5G pods are installed for connection. 
Post-Condition
  •  
  • Health alerts handled; intervention performed if needed.
StatusHazards marked and/or victims detected; team proceeds with increased safety.

Action Sequence

  1. Deployment and Startup
     a. Team leader labels the buildings interior as robot-first (no human entry). 
     b. Robot Operator positions ANYmal at the threshold of the building while Analyst opens the control/analyst workspace of ANYmal in C3I.
     c. Wearables; Entry team confirms connection and baseline vitals. Safety officer sees green 'ready' status on the health/ location dashboard in C3I.
     d. Comms/ network connection check; confirms connection is stable for robot video/ LiDAR and wearable streams between worksite and HQ.
  2. Exploration and navigation
        a. ANYmal enters through the safest/ big enough opening with remote Operator tele-operating the tight passage.
        b. ANYmal builds a live 3D map (LiDAR) with XX sensors (only camera?) for victim detection; Analyst interprets layers of information.
        c. Real-time feed (video/ LiDAR/ thermal?) streams to C3I (to HQ); Analyst places markers (doors, unstable zones, possible victims).
  3. Victim detection and Robot/Operator control
        a. A candidate victim is detected on the LiDAR map/ camera view (with a confidence score?) by the Analyst; the Robot operator manually controls ANYmal to verify.
        b. If equipped, ANYmal's arm can nudge light debris or open door to approach the possible victim detection for verification.
        c. Victim locations are confirmed, tagged, and prioritized with access contraints (blocked corridor, gas risk, etc.) for Team leader's plan.
  4. Team coordination and decision support
     a. An updated worksite map (victims, no-go areas, suggested routs?) is visable on the Team leader's tablet; and addional information is communicated with the Team leader by the Analyst.
     b. Team leader refines the plan (who enters, route, tools) and briefs Entry team. Decision rationale is logged in C3I for HQ. 
     c. ..
  5. Remote monitoring and escalation
     a. Part of the entry team is now inside of the building; while safety officer at HQ verfies continious FR vitals and positioning.
     b. If critical threshold is crossed (e.g. high heart rate/ temperature spike), Team leader recieves an on-dashboard critical alert while the affected FR (or/and buddy?) sees a local alarm (LED/ audio/ haptic).
     c. Escallation policy: alarms .

Claims (title)FunctionEffect(s)Action Sequence Step(s)
CL1 Improve safety for respondersRobot enters unsafe or blocked zonesEFXX: Improves safety of responder by having robot explore unsafe or blocked zones instead 
→ Measured with FR path logs or near-incident reports.		2a
CL2 Improved FR SA SNAKE camera visual confirmationEFXX: Improves SA of FR by inspecting small spaces 
→ Measure with NASA TLX? 		4b
CL2 Improved victim detection rate in small spacesSNAKE camera visual confirmationEFXX: Victim discovery rate increased
→ Measured with number of victims detected & time to victim detection, compared to manual entry.		2b, 2c
CL3 Safer responder routingHazard identification via robotEFXX: First responder safety increased
→ Measured with map updates and avoided hazards.		2c, 3a
CL4 Trustworthy SAOperator-validated visualsEFXX: Trust in robot findings increased by operator confirmation
→ Measured with trust survey or follow-up interviews.		4a
CL5 Operational speed improvedRobot scouts ahead of teamEFXX: Operational speed improved
→ Measured with time-per-room metrics.		2a, 3c