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

2025-05-12

AI Summary

To address the neglect of terrain heterogeneity in multi-UAV path planning for search-and-rescue (SAR) missions, this paper proposes a terrain-aware cooperative path planning framework. First, a fine-tuned deep semantic segmentation network processes satellite imagery to automatically classify land-cover types. Second, a gridded terrain model is constructed, incorporating terrain monotonicity assessment and a two-stage adaptive regional partitioning mechanism that jointly leverages remote-sensing semantic segmentation and cost-based recursive partitioning. Finally, terrain-informed task allocation and efficient trajectories are generated within a high-fidelity SAR simulation environment. Experimental results demonstrate that the proposed method significantly reduces search and response times, outperforming mainstream metaheuristic algorithms and state-of-the-art baselines. It enables scalable, rapid, and coordinated SAR operations over large-scale, complex terrains.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Terrain-aware path planning for UAV swarms in SAR missions
• Autonomous terrain analysis for optimized UAV deployment
• Irregular terrain partitioning for efficient UAV task allocation

Innovation

• Deep segmentation network for terrain classification
• Two-stage partitioning scheme for irregular terrain
• Computer-vision based terrain-aware mission planner