Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2501.06566 v2 pith:P3CE6OTU submitted 2025-01-11 cs.RO cs.SYeess.SY

classification cs.ROcs.SYeess.SY
keywords inspectioncariccooperativemulti-uavplanningtaskteamsaerial
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. FleetScape: A Mixed Reality Sandtable for Spatial Supervision and Control of Scalable Drone Fleets

    cs.HC 2026-07 conditional novelty 6.0 of 10

    A mixed-reality 3D sandtable lets single operators supervise fleets of 5–15 simulated inspection drones, with situational awareness degrading beyond roughly 10 drones.

Pith tools