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Yuanyuan Du

3 accepted papers

2025

Cooperative Motion Planning in Divided Environments via Congestion-Aware Deep Reinforcement Learning

RA-L 2025

In motion planning with partial observability, addressing uncertainty is crucial for preventing collisions and congestion, especially in the vicinity of constrained narrow areas connecting wider spaces, called hallways. In this work, we propose a cooperative motion planning algorithm that leverages

Cited by 5SourceScholar
2025

Swept Volume-Based Continuous Object Gathering Trajectory Generation for Tethered Robot Duo

IROS 2025

We propose a continuous gathering scheme based on the swept volume to address the challenges involved in planning a tethered robot duo to efficiently collect marine debris. Specifically, we model the tethered robot duo by constructing a double-layer U-shape, and then apply an object-aware optimizati

Cited by 0SourceScholar
2023

Global Map Assisted Multi-Agent Collision Avoidance via Deep Reinforcement Learning around Complex Obstacles

IROS 2023poster

State-of-the-art multi-agent collision avoidance algorithms face limitations when applied to cluttered public environments, where obstacles may have a variety of shapes and structures. The issue arises because most of these algorithms are agent-level methods. They concentrate solely on preventing co…

Cited by 4SourceScholar