HydraCollab: Adaptive Collaborative-Perception for Distributed Autonomous Systems
2026-06-30 • Robotics
RoboticsArtificial IntelligenceComputer Vision and Pattern RecognitionMachine LearningMultiagent Systems
AI summaryⓘ
The authors developed HydraCollab, a system that helps multiple robots share important sensor information without using too much communication bandwidth. It smartly picks which sensor data to send and adjusts how the robots collaborate based on how confident they are about what they sense around them. Tested on several datasets, HydraCollab used much less bandwidth than previous methods while still improving perception accuracy. This makes robot teamwork more efficient when communication is limited.
collaborative perceptionmulti-robot systemscommunication bandwidthsensor featuresspatial confidence mapsintermediate collaborationlate collaborationV2X-R datasetV2X-Radar datasetUAV3D-mini dataset
Authors
Luke Chen, Cheng-Ju Wu, David R. Martin, Qilin Ye, Pramod Khargonekar, Mohammad Abdullah Al Faruque
Abstract
Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative-perception systems face an inherent trade-off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better perception results at the cost of increased communication overhead. However, real-world communication networks impose bandwidth constraints that require minimizing communication overhead without sacrificing perception performance. To address this challenge, we propose HydraCollab, an adaptive collaborative-perception framework that (i) selectively transmits the most informative sensor features and (ii) dynamically employs collaboration strategies (intermediate or late) based on spatial confidence maps. Extensive evaluations on the V2X-R, V2X-Radar and UAV3D-mini datasets demonstrate that HydraCollab achieves the best overall trade-off between accuracy and communication cost among existing collaborative-perception methods. Relative to SOTA Where2comm, HydraCollab uses only 41% of the bandwidth on V2X-R and 26% on V2X-Radar while improving performance by 0.78% and 0.75% respectively. Our code and models are available at https://github.com/AICPS/HydraCollab.