Chao Cao

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

I served as the planning team lead of Team Explorer that took part in the DARPA Subterranean Challenge. Check out this cool video from Microsoft and our exploring robots at the Urban Circuit and Final Event!

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

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News

Jul 2023: Paper on Autonomous Exploration published on Science Robotics.
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. The first time that one paper won both awards in the history of RSS!
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. The succeeding work is published on Science Robotics, which extends to multi-robot exploration.

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