3 papers
cs.RO2026
GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning
Jonas le Fevre Sejersen, Toyotaro Suzumura, Erdal Kayacan
This paper presents a novel approach to multi-robot collision avoidance that integrates global path planning with local navigation strategies, utilizing attentive graph neural netw…
cs.MA2025
CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots
Jonas le Fevre Sejersen, Erdal Kayacan
This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructure…
cs.RO2025
Multi-Agent Path Planning in Complex Environments using Gaussian Belief Propagation with Global Path Finding
Jens Høigaard Jensen, Kristoffer Plagborg Bak Sørensen, Jonas le Fevre Sejersen +1
Multi-agent path planning is a critical challenge in robotics, requiring agents to navigate complex environments while avoiding collisions and optimizing travel efficiency. This wo…