Land-Coverage Aware Path-Planning for Multi-UAV Swarms in Search and Rescue Scenarios (2505.08060v1)

Abstract: Unmanned Aerial Vehicles (UAVs) have become vital in search-and-rescue (SAR) missions, with autonomous mission planning improving response times and coverage efficiency. Early approaches primarily used path planning techniques such as A*, potential-fields, or Dijkstra's algorithm, while recent approaches have incorporated meta-heuristic frameworks like genetic algorithms and particle swarm optimization to balance competing objectives such as network connectivity, energy efficiency, and strategic placement of charging stations. However, terrain-aware path planning remains under-explored, despite its critical role in optimizing UAV SAR deployments. To address this gap, we present a computer-vision based terrain-aware mission planner that autonomously extracts and analyzes terrain topology to enhance SAR pre-flight planning. Our framework uses a deep segmentation network fine-tuned on our own collection of landcover datasets to transform satellite imagery into a structured, grid-based representation of the operational area. This classification enables terrain-specific UAV-task allocation, improving deployment strategies in complex environments. We address the challenge of irregular terrain partitions, by introducing a two-stage partitioning scheme that first evaluates terrain monotonicity along coordinate axes before applying a cost-based recursive partitioning process, minimizing unnecessary splits and optimizing path efficiency. Empirical validation in a high-fidelity simulation environment demonstrates that our approach improves search and dispatch time over multiple meta-heuristic techniques and against a competing state-of-the-art method. These results highlight its potential for large-scale SAR operations, where rapid response and efficient UAV coordination are critical.

• The paper introduces a novel terrain-aware path planning system for multi-UAV swarms in search and rescue, utilizing computer vision to classify land coverage from satellite imagery.
• Empirical simulations demonstrate that this terrain-aware approach significantly improves search response time and dispatch efficacy compared to contemporary methods.
• This system optimizes the strategic deployment of diverse UAV capabilities based on specific terrain types, reducing manual planning effort and accelerating mission readiness.

Land-Coverage Aware Path-Planning for Multi-UAV Swarms in Search and Rescue Scenarios: A Review

The paper discusses a sophisticated approach to improving unmanned aerial vehicle (UAV) deployments in search-and-rescue (SAR) scenarios through land-coverage aware path-planning algorithms. UAVs have consistently been integral components of SAR missions, yet traditional path planning techniques—such as A*, potential fields, and Dijkstra's algorithm—have been limited by their deterministic nature, focusing primarily on minimizing path length or travel cost while avoiding collisions. However, contemporary methods incorporating metaheuristics, like genetic algorithms (GAs) and particle swarm optimization (PSO), have broadened this approach by addressing competing objectives such as network connectivity, energy efficiency, and strategic station placements.

Despite advancements in multi-objective optimization algorithms, terrain-awareness remains under-explored in UAV path planning techniques for SAR missions. This paper introduces a novel system that incorporates computer vision (CV) and deep learning to recognize and classify terrain topology, which enhances UAV task allocation strategies across diverse environments. By utilizing satellite imagery, the framework employs CV models to segment terrain into labeled, grid-based regions, enabling terrain-specific UAV-task assignments founded on the spatial and critical operational demands of SAR deployments.

A two-stage partitioning scheme is proposed by the authors to refine terrain segmentation further, minimizing path inefficiencies induced by irregular terrain partitions. This involves initially evaluating terrain monotonicity along coordinate axes and then implementing a cost-based recursive partitioning method to optimize path efficiency without unnecessary subdivisions. An empirical evaluation carried out through high-fidelity simulation demonstrated sweeping improvements in search response time and dispatch efficacy in comparison to numerous contemporary metaheuristic methods.

Importantly, this advancement in terrain-aware mission planning presents a significant implication for UAV-assisted SAR operations. By diligently recognizing terrain features, the proposed framework optimizes UAV heterogeneity, effectively deploying UAVs equipped with varying sensors and operational capabilities to areas where they are most efficient—such as LiDAR-equipped UAVs in forested areas or multispectral cameras over water bodies. Furthermore, this automation reduces the manual input required in formulating terrain-based strategies, expediting mission readiness and scalability across extensive operational environments.

The practical and theoretical implications of this research are substantial. Practically speaking, faster and more precise path planning potentially translates into timely relief efforts in large-scale emergencies, ultimately saving more lives. From a theoretical perspective, the paper posits an intriguing research direction: integrating AI-based terrain-aware frameworks with autonomous UAV operation can refine SAR operations substantially. Additionally, further developments in CV and segmentation accuracy will likely continue to enhance these processes by providing more precise inputs for mission planning algorithms.

As the field progresses, prospective research could explore the integration of real-time dynamic environmental updates into the mission planning algorithms, improving response efficacy further under fluctuating conditions often encountered in SAR missions. Moreover, as UAV capabilities continue to diversify, it is imperative to examine the optimal alignment of novel payloads and sensors with evolving terrain-based requirements to streamline future operations further.

This paper presents robust empirical evidence showcasing the efficacy of terrain-aware path planning in improving SAR mission efficiency. The adoption of CV-based terrain analysis into UAV path planning processes is a noteworthy contribution to SAR operations, positioning the research as an integral building block for future advancements in autonomous UAV technology for disaster response.