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

We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-uncrewed-aerial-vehicle (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 the 2023 IEEE Conference on Decision and Control (CDC) and the IROS 2024 Workshop on Multi-Robot Perception and Navigation, attracting innovative solutions from research teams worldwide. This article 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 research directions in cooperative multi-UAV systems. The simulation framework, including the source code and detailed instructions, is publicly available at <ext-link ext-link-type="uri" xlink:href="https://ntu-aris.github.io/caric" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ntu-aris.github.io/caric</ext-link>.

Authors

Institutions

Publication Details

Journal
IEEE Robotics & Automation Magazine
Published
2025-01-01
DOI
https://doi.org/10.1109/mra.2025.3584341
Citations
10
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
49.94
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Panayiotis Kolios, Savvas Papaioannou, Antreas Anastasiou, Shenghai Yuan et al.
10 citations
IEEE Robotics & Automation Magazine
Robotics and Sensor-Based Localization
49.94
article

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

Panayiotis Kolios, Savvas Papaioannou, Antreas Anastasiou, Shenghai Yuan, Angelos Zacharia, Muqing Cao, Xinhang Xu, Christos G. Panayiotou, Thien‐Minh Nguyen, Fei Gao, Marios M. Polycarpou, Mingjie Zhang, Lihua Xie, Ben M. Chen, Boyu Zhou
article en
10 citations

Abstract

We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-uncrewed-aerial-vehicle (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 the 2023 IEEE Conference on Decision and Control (CDC) and the IROS 2024 Workshop on Multi-Robot Perception and Navigation, attracting innovative solutions from research teams worldwide. This article 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 research directions in cooperative multi-UAV systems. The simulation framework, including the source code and detailed instructions, is publicly available at <ext-link ext-link-type="uri" xlink:href="https://ntu-aris.github.io/caric" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ntu-aris.github.io/caric</ext-link>.

IEEE Robotics & Automation Magazine
Nanyang Technological University (SG), Chinese University of Hong Kong (HK), Southern University of Science and Technology (CN), University of Cyprus (CY), Carnegie Mellon University (US), Zhejiang University (CN)
Openalex Percentile: Top 0%
Robotics and Sensor-Based Localization
49.94
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.