Chao Cao

I am a third-year Ph.D. student at The Robotics Institute, Carnegie Mellon University advised by Ji Zhang and Howie Choset.

I serve as the planning team lead of Team Explorer competing in the DARPA Subterranean Challenge. For a teaser, check out this cool video from Microsoft and the compilation of our Urban Circuit performance !

I have an MS in Robotics from CMU RI, where I was advised by Matt Travers and Howie Choset. I obtained my BS in Computer Science from The Univeristy of Hong Kong, where I worked with Jia Pan and Wenping Wang.

I'm interested in robot navigation and motion planning. My recent work has been on exploration and sensor coverage planning.

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News

Oct 2022: Paper on Route Planning won the Best Student Paper Award of IROS 2022.
Sep 2021: Came in forth place in the SubT Final Circuit. Our multi-robot exploration algorithm led to winning the "Most Sectors Explored Award".
Sep 2021: Awarded the CMU Presidential Fellowship.
Jul 2021: Paper on Autonomous Exploration won the Best Paper Award and Best Systems Paper Award of RSS 2021.
Feb 2020: Came in second place in the SubT Urban Circuit.
Jan 2020: Paper on Coverage Path Planning accepted to ICRA 2020.
Aug 2019: Came in first place in the SubT Tunnel Circuit.
Jan 2019: Paper on Crowd Navigation accepted to ICRA 2019.

Selected Research Projects

TARE: A Hierarchical Framework for Efficiently ExploringComplex 3D Environments

Chao Cao, Hongbiao Zhu, Howie Choset, Ji Zhang
Robotics: Science and Systems(RSS), 2021
Best Paper Award and Best Systems Paper Award
website / video /

A hierarchical framework for autonomous exploration in large-scale environments. This work won the Best Paper Award and Best Systems Paper Award of RSS 2021, which is the first time that one paper won both awards in the history of RSS.

Hierarchical Coverage Path Planning in Complex 3D Environments

Chao Cao, Ji Zhang, Matt Travers and Howie Choset
IEEE International Conference on Robotics and Automation (ICRA), 2020
video /

A multi-resolution hierarchical framework for sensor coverage planning. It solves a high-level Traveling Salesman Problem (TSP) for a global tour, which is then used for assembling low-level trajectories obtained by solving local Orienteering Problems. The hierarchical scheme produces much higher efficiency than the state-of-the-art.

Dynamic Channel: A Planning Framework for Crowd Navigation

Chao Cao, Peter Trautman and Soshi Iba
IEEE International Conference on Robotics and Automation (ICRA), 2019
arxiv / website / video /

A hierarchical planning framework for crowd navigation. It incorporates a global planner to find feasible paths in the topological space efficiently, and a local planner to follow the path with optimized trajectories.

Who Visited Me

Design and source code from Leonid Keselman's website