Dynamic Decentralized 3D Urban Coverage and Patrol with UAVs

TL;DR

This work addresses decentralized, swarm-based 3D urban coverage and patrol in disaster scenarios by discretizing the area into viewpoint-based closed paths and allocating UAVs to these paths. A four-part modular pipeline—Viewpoint Generation, Task Generation, Task Allocation, and Patrol Strategy—enables scalable, fault-tolerant operation using local information: CBBA/CBGA for decentralized task allocation and a minimal bounce-based patrol rule for emergent, periodic coverage. Simulations over a 7-building urban model with 401 viewpoints and 100 agents demonstrate convergence to a bounded maximum viewpoint idleness $i_v$ and complete coverage, with only local communications and offline path planning. The approach offers a practical, robust framework for urban surveillance with resource-constrained UAVs, trading optimality for scalability and resilience in dynamic environments.

Abstract

In the event of natural or man-made disasters in an urban environment, such as fires, floods, and earthquakes, a swarm of unmanned aerial vehicles (UAVs) can rapidly sweep and provide coverage to monitor the area of interest and locate survivors. We propose a modular framework and patrol strategy that enables a swarm of UAVs to perform cooperative and periodic coverage in such scenarios. Our approach first discretizes the area of interest into viewpoints connected via closed paths. UAVs are assigned to teams via task allocation to cooperatively patrol these closed paths. We propose a minimal, scalable, and robust patrol strategy where UAVs within a team move in a random direction along their assigned closed path and "bounce" off each other when they meet. Our simulation results show that such a minimal strategy can exhibit an emergent behaviour that provides periodic and complete coverage in a 3D urban environment.

Figures (10)

• Figure 1: Example of viewpoints generated for a single building.
• Figure 2: Example of viewpoints with closed path.
• Figure 3: Agents $i$ and $k$, moving in opposite directions, have both selected the same viewpoint $v = c_i = c_k$, but since agent $i$ is nearer, it continues to service $v$ and changes direction after that, while agent $k$ changes direction immediately.
• Figure 4: Agents $i$ and $k$, moving in the same direction, have both selected the same viewpoint $v = c_i = c_k$, but since agent $i$ is nearer, it continues to service $v$ and move in the same direction while agent $k$ changes direction immediately.
• Figure 5: Agents $i$ and $k$, moving in the opposite directions and having selected each other's last viewpoints, have just come into communications range. Both agents reverse direction immediately.