Safe Multi-Robot Coordination via VLM-LLM Reasoning and Reachability Analysis

Mohamed Dwedar; Ahmad Hafez; Alexander Jesser; Amr Alanwar

cs.ROAug 18, 2026

Safe Multi-Robot Coordination via VLM-LLM Reasoning and Reachability Analysis

Authors: Mohamed Dwedar, Ahmad Hafez, Alexander Jesser, Amr Alanwar

Organizations: TUM School of Computation, Information, and Technology (CIT), Technical University of Munich (TUM), Heilbronn, Germany. · Institute for Intelligent Cyber-Physical Systems (ICPS), Heilbronn University of Applied Science, Heilbronn, Germany.

Abstract

Safe coordination in heterogeneous machine-to-machine (M2M) robotic systems is challenging when robots differ in sensing capabilities, environmental awareness, and motion execution roles. This paper presents a centralized safety-aware M2M framework for cooperative goal-directed navigation in a heterogeneous mobile robot team comprising a vision-capable quadruped and a camera-less robotic vehicle. The objective is to guide both platforms toward a goal region while avoiding static and dynamic obstacles and preventing unsafe inter-robot interactions. Under the principle of shared perception, the vision-capable robot provides semantic environmental awareness through a centralized server over an MQTT broker, enabling the camera-less platform to navigate using this shared scene representation alongside its own odometry, IMU, and state feedback. A vision-language model (VLM) interprets the visual stream, and the extracted semantic data is mapped into conservative metric geometric constraints, including inflated obstacle sets, safe corridors, and goal regions. A large language model (LLM) proposes high-level task allocations, while physical command authority is restricted to a robot-specific zonotope reachability gate. This verification engine propagates independent reachable tubes to evaluate obstacle avoidance, safe-corridor containment, and inter-robot separation predicates before approving commands. Online experiments across clear-path and dynamic-obstacle scenarios show that the pipeline reliably approves safe motion, triggers conservative replanning or holding maneuvers upon constraint violation, and enforces a strict architectural separation between advisory semantic reasoning and formally verified motor execution.

Figures & tables

Fig. 1 : Simplified view of the proposed method. Shaded blocks are gates; light blocks are advisory and carry no command authority. Semantic evidence produced by the Go2 propagates to SVEA, but command authority does not: Gate 4 is evaluated independently for each robot.

		Capability axis
System	Category	SEM	SYM	GATE	MR	XFER	INT	Safety mechanism reported
Language-model planning and vision–language–action models
SayCan [ 6 ]	LLM planning	✓	✓	✗	✗	✗	✗	Affordance scoring; no kinodynamic verification.
PaLM-E [ 11 ]	VLA model	✓	✓	✗	✗	✗	✗	End-to-end; safety implicit in training data.
RT-2 [ 40 ]	VLA model	✓	(✓)	✗	✗	✗	✗	Co-trained action tokens; no runtime gate.
Code-as-Policies [ 23 ]	LLM code generation	✓	✓	✗	✗	✗	✗	Programmatic guards written by the model itself.

TABLE I : Capability comparison against representative systems from the three literatures the proposed pipeline draws on. ✓ provided; (✓) partial, offline, or informal; ✗ not provided. The final row tallies how many of the twelve prior systems cover each axis.

Fig. 2 : Centralized M2M multi-robot safety decision pipeline.

Component	Pipeline Role	Produced Information	Command Authority
Unitree Go2	Vision-capable robot; publishes camera frames, odometry, IMU, and robot-state data over MQTT.	ROS 2 image topics; odometry, IMU, and state messages.	None; receives approved commands only.
SVEA robot car	Camera-less robot; publishes odometry, IMU, and state; executes independently verified robot-specific commands.	Odometry, IMU, and state messages; used for robot-specific reachability evaluation.	None; receives approved commands only.
Mosquitto MQTT	Asynchronous M2M transport for telemetry, status messages, and approved command packets.	Routed state messages, server status, and verified commands.	None; transport layer only.
Qwen2.5-VL via Ollama	Semantic interpretation of Go2 camera frames.	Schema-validated JSON scene output with labels, regions, and confidence values.	None; perception evidence only.
Qwen3 via Ollama	Symbolic task-level reasoning from validated scene context and robot states.	Symbolic task proposal for downstream reachability verification.	None; symbolic guidance only.
Python zonotope module	CORA-style set propagation; robot-specific predicate evaluation.	Safety flag $\sigma_{k,i}$ and approved command $u_{k,i}^{\mathrm{safe}}$ .	Sole command-approval authority.

TABLE II : System components, pipeline roles, and command authority.

Fig. 3 : Online clear-path validation from the Go2 camera stream.

Fig. 4 : Corridor-containment in the local workspace.

Fig. 5 : Online dynamic-obstacle validation from the Go2 camera stream.

Fig. 6 : Reachability under dynamic-obstacle interaction.

Fig. 7 : Reconstructed Go2 and SVEA planar trajectories.

Fig. 8 : Confidence, reprojection error, and safety-radius surface.

Fig. 9 : VLM confidence versus conservative safety radius.

ID	Scenario	Cycles	Duration (s)	Objective	Predicate stressed
S1	Office room, goal approach	720	24.0	Establish nominal acceptance: verify the gate approves motion when a traversable corridor and a goal cue are both present and no dynamic obstacle is in the workspace.	$\eta^{\mathrm{obs}}$ , $\eta^{\mathrm{cor}}$
S2	Stairwell, non-traversable hazard	361	12.0	Establish maximal rejection: verify that a region which is geometrically open but not traversable is excluded by corridor containment even though no obstacle intersection occurs.	$\eta^{\mathrm{cor}}$ (isolated)
S3	Corridor, pedestrian entry	421	14.0	Verify dynamic-obstacle handling: compound inflation of a moving human footprint and redirection of the candidate path through the remaining traversable corridor.	$\eta^{\mathrm{obs}}$
S4	Sustained human tracking	451	15.0	Verify sustained operation under continuous re-planning at close range, including decision stability and clearance maintenance.	$\eta^{\mathrm{obs}}$ , $\eta^{\mathrm{cor}}$
Total		1,953	65.1	$17{,}577$ verified candidate arcs, $N=15$ steps each

TABLE III : Test matrix: evaluation scenarios, objectives, and the safety predicate each is designed to exercise.

ID	Scenario	Cycles	Arcs	Approved	Withheld	Safe	Warning	Unsafe	Mean risk
		Volume		Gate outcome (%)		Internal safety state (%)
S1	Office, goal approach	720	6,480	56.4	43.6	92.2	7.8	0.0	$0.187\pm 0.092$
S2	Stairwell hazard	361	3,249	0.0	100.0	48.5	51.5	0.0	$0.341\pm 0.126$
S3	Corridor, pedestrian	421	3,789	47.7	52.3	78.9	17.1	4.0	$0.272\pm 0.186$
S4	Sustained tracking	451	4,059	37.9	62.1	18.8	12.6	68.5	$0.678\pm 0.230$
All scenarios		1,953	17,577	39.8	60.2	64.3	19.0	16.7	$\mathbf{0.347\pm 0.248}$

TABLE IV : Gate behavior per evaluation scenario. Approval and withholding are reported as fractions of decision cycles; the internal safety state is the fraction of cycles spent in each condition; risk is the mean cycle-level score

\pm

one standard deviation.

Motion approved
Decision	Cycles	Share	Mean risk	Clearance (m)
PROCEED	247	$12.6\%$	$0.155$	$2.43$
AVOID $+$ PROCEED	201	$10.3\%$	$0.737$	$0.92$
TURN LEFT	170	$8.7\%$	$0.300$	$1.70$
TURN RIGHT	160	$8.2\%$	$0.167$	$1.56$
Motion withheld

TABLE V : Statistics conditioned on the decision issued by the gate. Clearance is the mean distance to the closest detected obstacle in cycles carrying that decision.

Unresolved confidence: 89 ( $9.1\%$ ), treated as maximum uncertainty.
Class	Detections	Confidence	Range (m)	Min (m)
Person	456	$0.870\pm 0.068$	$0.91\pm 0.26$	$0.51$
Chair	404	$0.842\pm 0.083$	$3.19\pm 2.94$	$0.99$
Cabinet	113	$0.613\pm 0.086$	$1.00\pm 0.42$	$0.64$
All	973	$0.844\pm 0.095$	$1.86\pm 2.21$	$0.51$
Dynamic: 456 ( $46.9\%$ ); static: 517 ( $53.1\%$ ).

TABLE VI : Perception statistics over the

973

detections admitted to geometric conversion.

Condition	Active Predicate(s)	Decision	Status
All predicates satisfied	$\eta^{\mathrm{obs}}\land\eta^{\mathrm{cor}}\land\eta^{\mathrm{int}}$	PROCEED	Approved
Obstacle present; alt. path available	$\eta^{\mathrm{obs}}\land\eta^{\mathrm{cor}}\land\eta^{\mathrm{int}}$ (after redirect)	AVOID+PROCEED	Approved
Obstacle-free; corridor violated	$\eta^{\mathrm{obs}}\land\neg\eta^{\mathrm{cor}}\land\eta^{\mathrm{int}}$	REPLAN	Withheld
Obstacle intersection	$\neg\eta^{\mathrm{obs}}$	STOP/HOLD	Withheld
Inter-robot overlap	$\neg\eta^{\mathrm{int}}$	HOLD	Withheld
Synchronization failure	Gate 1 (Eq. ( 2 ))	HOLD/STOP	Withheld

TABLE VII : Reachability gate decision logic.

Pipeline Stage	Symbol	Range (ms)	Budget (ms)	Rationale
MQTT transport	$T_{\mathrm{MQTT}}$	5–25	15	Local LAN or loopback broker; single-hop, low contention.
Synchronization & validation	$T_{\mathrm{sync}}$	1–8	5	Timestamp-pair checking, alignment, duplicate and out-of-order rejection.
VLM inference & JSON validation	$T_{\mathrm{VLM}}$	350–1200	750	Qwen2.5-VL via Ollama; range depends on GPU availability, image resolution, and prompt complexity.
Semantic-to-geometric conversion	$T_{\mathrm{geo}}$	5–35	20	Homography projection, Minkowski inflation, corridor and goal-region construction.
LLM symbolic reasoning	$T_{\mathrm{LLM}}$	120–600	300	Qwen3 via Ollama processing compact JSON scene context and robot states.
Reachability propagation ( ${\times}2$ )	$T_{\mathrm{reach}}$	20–160	100	Short-horizon zonotope propagation and three-predicate evaluation per robot; $50\,\mathrm{ms}{\times}2$ .

TABLE VIII : Representative per-cycle design-time latency budget.

ID	Condition	Pipeline Modification	Isolated Contribution
B1	No reachability gate	Reachability module disabled; LLM proposals published without geometric verification.	Role of the reachability gate in preventing unsafe command execution.
B2	No VLM perception	VLM removed; geometric conversion receives no semantic scene evidence.	Role of online semantic perception in constructing obstacle sets, safe corridors, and goal regions.
B3	VLM $+$ LLM without reachability	Both language models retained; reachability gate removed.	Whether language-model outputs alone suffice for safe command publication.
B4	No shared perception for SVEA	SVEA does not receive Go2-derived scene evidence and relies only on its own state feedback.	Role of shared perception for the camera-less robot’s safety constraint construction.
A1	Obstacle inflation sweep	$r_{\mathrm{safe}}$ varied; remaining pipeline unchanged.	Sensitivity of command approval rate to obstacle buffering.
A2	Perception uncertainty sweep	$r_{\mathrm{perc}}$ varied with VLM confidence.	Sensitivity to semantic uncertainty in the safety-margin model.

TABLE IX : Baseline and ablation evaluation conditions.

Safe Robot Navigation

Safe robot navigation develops methods for reaching goals while avoiding collisions and managing uncertainty, dynamic obstacles, and environmental hazards.

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Abstract

Safe Robot Navigation

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