works on

From the 1 of 6 linked papers with an AI index.

collaborators

6 papers

cs.LG2026

A Multi-Agent Framework for Zero-Dimensional Reduced-Order Model Planning

Bingteng Sun, Hao Yin, Yiling Chen +9

The paper introduces Z-COPA, a multi‑agent framework that uses a symbolic graph engine and MILP‑guided optimization to automate the planning of zero‑dimensional reduced‑order model…

cs.RO2026

An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency

Wule Mao, Zhouheng Li, Entao Sun +2

Generating overtaking trajectories in high-speed scenarios is typically addressed through hierarchical planning, which often suffers from local optima due to single initial solutio…

cs.RO2025

A Rapid Iterative Trajectory Planning Method for Automated Parking through Differential Flatness

Zhouheng Li, Lei Xie, Cheng Hu +1

As autonomous driving continues to advance, automated parking is becoming increasingly essential. However, significant challenges arise when implementing path velocity decompositio…

cs.RO2025

Learning to Drift in Extreme Turning with Active Exploration and Gaussian Process Based MPC

Guoqiang Wu, Cheng Hu, Wangjia Weng +4

Extreme cornering in racing often leads to large sideslip angles, presenting a significant challenge for vehicle control. Conventional vehicle controllers struggle to manage this s…

eess.SY2025

Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives

Runze Lin, Junghui Chen, Lei Xie +1

In the context of Industry 4.0 and smart manufacturing, the field of process industry optimization and control is also undergoing a digital transformation. With the rise of Deep Re…

eess.SY2025

Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition

Zhouchang Li, Runze Lin, Hongye Su +1

In the era of smart manufacturing and Industry 4.0, the refining industry is evolving towards large-scale integration and flexible production systems. In response to these new dema…