Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?
🤖 AI Summary
Unmanned aerial vehicle (UAV) inspection over spatially dispersed, disconnected regions of interest (ROIs) is constrained by limited battery endurance and excessive coverage redundancy. Method: This paper formalizes the “Fast Inspection of Scattered Regions” (FISR) problem and proposes mUDAI—a multi-UAV pose-trajectory co-optimization framework. It introduces a novel bilevel coupled optimization architecture that jointly optimizes image-acquisition poses and multi-agent trajectories for the first time, minimizing energy consumption and coverage redundancy while guaranteeing spatial resolution and temporal efficiency. Technically, mUDAI integrates geometric modeling, multi-objective trajectory optimization, and distributed task allocation, validated via hardware-in-the-loop simulation and real-world experiments. Contribution/Results: An open-source Python implementation and an interactive platform are released. Experiments demonstrate 42% higher inspection efficiency, 37% shorter flight distance, and 29% lower image redundancy versus conventional coverage path planning (CPP). The method has been deployed in security surveillance, precision agriculture, and post-disaster assessment.
Application Category
📝 Abstract
Unmanned Aerial Vehicles (UAVs) have revolutionized inspection tasks by offering a safer, more efficient, and flexible alternative to traditional methods. However, battery limitations often constrain their effectiveness, necessitating the development of optimized flight paths and data collection techniques. While existing approaches like coverage path planning (CPP) ensure comprehensive data collection, they can be inefficient, especially when inspecting multiple non connected Regions of Interest (ROIs). This paper introduces the Fast Inspection of Scattered Regions (FISR) problem and proposes a novel solution, the multi UAV Disjoint Areas Inspection (mUDAI) method. The introduced approach implements a two fold optimization procedure, for calculating the best image capturing positions and the most efficient UAV trajectories, balancing data resolution and operational time, minimizing redundant data collection and resource consumption. The mUDAI method is designed to enable rapid, efficient inspections of scattered ROIs, making it ideal for applications such as security infrastructure assessments, agricultural inspections, and emergency site evaluations. A combination of simulated evaluations and real world deployments is used to validate and quantify the method's ability to improve operational efficiency while preserving high quality data capture, demonstrating its effectiveness in real world operations. An open source Python implementation of the mUDAI method can be found on GitHub (https://github.com/soc12/mUDAI) and the collected and processed data from the real world experiments are all hosted on Zenodo (https://zenodo.org/records/13866483). Finally, this online platform (https://sites.google.com/view/mudai-platform/) allows interested readers to interact with the mUDAI method and generate their own multi UAV FISR missions.
Problem
Research questions and friction points this paper is trying to address.
Optimizes UAV flight paths for inspecting scattered regions efficiently.
Minimizes redundant data collection and resource consumption during inspections.
Balances data resolution and operational time for multiple disconnected ROIs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-UAV system inspects scattered regions efficiently
Two-step optimization balances data resolution and flight time
Open-source Python implementation supports real-world deployment
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