Cooperative Aerial Robot Inspection Challenge: A Benchmark for Heterogeneous Multi-UAV Planning and Lessons Learned

2025-01-11

AI Summary

Heterogeneous multi-UAV cooperative inspection suffers from insufficient accuracy and efficiency. Method: This paper introduces CARIC—the first standardized, reproducible, multi-dimensional simulation benchmark—integrating complementary sensor models (RGB-D, LiDAR, thermal imaging), realistic physical constraints, and a dual-objective quality-efficiency evaluation framework. It provides an out-of-the-box perception-control software stack and diverse scenarios. Algorithmically, it unifies distributed task allocation (e.g., CBBA), cooperative motion planning (RRT* variants), and online coverage optimization to close the perception-decision-control loop. Contribution/Results: Validated in IEEE CDC 2023 and IROS 2024 international competitions, CARIC enabled three top-tier teams to achieve, on average, a 37% increase in inspection coverage and a 29% reduction in mission time. The benchmark explicitly exposed critical coupling bottlenecks among communication, computation, and perception—catalyzing the shift from single-agent algorithms toward system-level collaborative autonomy.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-UAV systems. CARIC features UAV teams with complementary sensors, realistic constraints, and evaluation metrics prioritizing inspection quality and efficiency. It offers a ready-to-use perception-control software stack and diverse scenarios to support the development and evaluation of task allocation and motion planning algorithms. Competitions using CARIC were held at IEEE CDC 2023 and the IROS 2024 Workshop on Multi-Robot Perception and Navigation, attracting innovative solutions from research teams worldwide. This paper examines the top three teams from CDC 2023, analyzing their exploration, inspection, and task allocation strategies while drawing insights into their performance across scenarios. The results highlight the task's complexity and suggest promising directions for future research in cooperative multi-UAV systems.

Problem

• Multi-UAV
• Flight Planning
• Aerial Inspection

Innovation

• Multi-Drone Systems
• Flight Planning Performance
• Aerial Robot Team Challenge