Institution profile

Luleå University of Technology

Academic institutioneurope · se
Official website
Research library95linked papers
Opportunities0open roles
Selected work

Representative Papers

An Addendum to NeBula: Toward Extending Team CoSTAR’s Solution to Larger Scale Environments

Apr 18, 2025IEEE Transactions on Field Robotics

Autonomous collaborative exploration in ultra-large-scale, unstructured underground environments remains challenging due to severe communication constraints, navigation uncertainty, and lack of prior maps. Method: This work extends TEAM CoSTAR’s NeBula autonomy system with a full-stack enhancement framework integrating semantic-geometric joint mapping, distributed POMDP-based global planning under communication constraints, adaptive filtering for localization, Gaussian process–based probabilistic traversability modeling, edge-cloud cooperative communication protocols, and aerial-ground heterogeneous multi-agent task allocation. Contribution/Results: The framework achieves, for the first time, robust long-range mapping (>5 km²), sub-meter localization accuracy (<0.3 m), and decentralized collaborative decision-making in kilometer-scale underground spaces (e.g., limestone mines). Validated in the DARPA Subterranean Challenge and real-world mine deployments, it improves mission completion rate by 37%, significantly advancing scalability, robustness, and coordination in autonomous underground exploration.

6 citationsRead paper

Safe Heterogeneous Multi-Agent RL with Communication Regularization for Coordinated Target Acquisition

Jan 13, 2026

This work addresses the challenges of safety and coordination in decentralized multi-agent systems operating under partial observability, limited communication, and dynamic interactions while collaboratively discovering and capturing randomly appearing targets. To this end, the authors propose a decentralized multi-agent reinforcement learning framework that integrates a graph attention network (GAT) encoder to fuse local observations with neighboring agents’ communicated information, incorporates a safety filter to ensure trajectory safety, and introduces a structured reward function that encourages orthogonality among communication vectors to enhance collaborative efficiency. Experimental results based on the MAPPO algorithm demonstrate that the proposed method outperforms baseline approaches in task success rate, obstacle avoidance, communication decorrelation, and training stability. Ablation studies further confirm the effectiveness of the designed reward mechanism.

1 citationsRead paper
Recent publications

Latest Papers