Chongrong Fang

About

Chongrong Fang is a rising researcher in robotics and multi-agent systems, with a focus on intelligent motion planning, perception-driven manipulation, and security in networked robot teams. His work addresses critical challenges in dynamic and cluttered environments, from intercepting moving targets with wheeled mobile robots—where his 2022 paper has garnered 13 citations—to enabling robots to grasp occluded objects through his affordance-driven next-best-view planning policy (ACE-NBV), published in 2023. Fang also advances deep reinforcement learning for mapless, multi-robot path planning in complex, unknown settings, and explores the vulnerability of multi-robot systems, proposing novel stealthy attack strategies against coverage path planning and multi-hop wireless networks. His research not only pushes the boundaries of autonomous navigation and manipulation but also highlights the fragility of cooperative robot networks, contributing to both performance and security. With a growing citation record and a portfolio spanning 2022–2023, Fang is establishing himself as a thoughtful contributor to the next generation of resilient, intelligent robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Moving Target Interception Considering Dynamic Environment

   13 citations · 2022
2. 2
   Affordance-Driven Next-Best-View Planning for Robotic Grasping

   6 citations · 2023
3. 3
   Mapless Path Planning of Multi-robot Systems in Complex Environments via Deep Reinforcement Learning

   4 citations · 2022
4. 4
   Optimal Attack Against Coverage Path Planning in Multi-robot System

   3 citations · 2022
5. 5
   Fragility-Aware Stealthy Attack Strategy for Multi-Robot Systems against Multi-Hop Wireless Networks

   2 citations · 2022

Key Collaborators

Contact & Links