Two-Layer Voronoi Coverage Control for Hybrid Aerial-Ground Robot Teams in Emergency Response: Implementation and Analysis

TL;DR

This work tackles rapid, heterogeneous deployment of aerial and ground robots for HazMat emergency sensing by introducing a decoupled two-layer Voronoi coverage framework. A bounded Voronoi solver with boundary interpolation enables real-time, importance-weighted planning, while an engineered composite importance function balances physical plume realism, numerical stability, and effective sensor allocation. The method resolves agent-trapping in clustered initial deployments and demonstrates an 88% reduction in time to target sensor coverage, validated through detailed simulations and open-source code. Practically, this approach accelerates threat diagnosis and reduces human exposure, with broad applicability to other time-critical disaster response scenarios.

Abstract

We present a comprehensive two-layer Voronoi coverage control approach for coordinating hybrid aerial-ground robot teams in hazardous material emergency response scenarios. Traditional Voronoi coverage control methods face three critical limitations in emergency contexts: heterogeneous agent capabilities with vastly different velocities, clustered initial deployment configurations, and urgent time constraints requiring rapid response rather than eventual convergence. Our method addresses these challenges through a decoupled two-layer architecture that separately optimizes aerial and ground robot positioning, with aerial agents delivering ground sensors via airdrop to high-priority locations. We provide detailed implementation of bounded Voronoi cell computation, efficient numerical integration techniques for importance-weighted centroids, and robust control strategies that prevent agent trapping. Simulation results demonstrate an 88% reduction in response time, achieving target sensor coverage (18.5% of initial sensor loss) in 25 seconds compared to 220 seconds for ground-only deployment. Complete implementation code is available at https://github.com/dHutchings/ME292B.

Figures (8)

• Figure 1: Typical HazMat transportation incident requiring rapid sensor deployment for threat assessment and monitoring.
• Figure 2: Tensegrity robots designed for airdrop deployment: (left) ground configuration with chemical sensors, (right) being carried by drone for rapid deployment to hazardous areas.
• Figure 3: Agent trapping: Red agent (fast) trapped behind slower agents, unable to use maximum velocity despite being targeted for a distant high-importance region.
• Figure 4: Composite importance function visualization for the Sarasota scenario showing the chemical plume dispersion model with wind effects. Higher intensity regions (shown in yellow/white) indicate areas requiring greater sensor coverage. The elongated shape reflects typical downwind dispersion patterns in outdoor chemical releases. Final agent positions are shown as colored markers.
• Figure 5: Performance comparison showing sensor loss, importance-weighted area coverage, and velocity profiles over time for the two-layer deployment method in the Sarasota scenario. Top panel shows how importance-weighted area is distributed among agents over time. Middle panel shows sensor loss (lower is better) decreasing sharply at approximately t=25s when the aerial agent performs its airdrop. Bottom panel shows velocity magnitudes, with the aerial agent (green) maintaining high speed until drop, while ground agents (blue, orange, red) operate near their maximum velocities during transit.