RSS 2021 Spotlight: TARE: A Hierarchical Framework for Efficiently Exploring Complex 3D Environments

Abstract
We present a method for autonomous exploration in complex three-dimensional (3D) environments. Our method demonstrates exploration faster than the current state-of-the-art using a hierarchical framework -- one level maintains data densely and computes a detailed path within a local planning horizon, while another level maintains data sparsely and computes a coarse path at the global scale. Such a framework shares the insight that detailed processing is most effective close to the robot, and gains computational speed by trading-off details far away from the robot. The method optimizes an overall exploration path with respect to the length of the path and produces a kinodynamically feasible local path. In experiments, our systems autonomously explore indoor and outdoor environments at a high degree of complexity, with ground and aerial robots. The method produces 80% more exploration efficiency, defined as the average explored volume per second through a run, and consumes less than 50% of computation compared to the state-of-the-art.

Authors
Chao Cao: http://caochao.me
Hongbiao Zhu: http://www.hongbiaoz.com
Howie Choset: http://www.cs.cmu.edu/~choset
Ji Zhang: https://frc.ri.cmu.edu/~zhangji

Paper
https://frc.ri.cmu.edu/~zhangji/publi...

Autonomous Exploration Development Environment
https://www.cmu-exploration.com

Ground-based Exploration - TARE Planner
https://www.cmu-exploration.com/tare-...

Aerial Exploration - A-TARE Planner
https://www.cmu-exploration.com/a-tar...

Other Related Work
C. Cao, H. Zhu, Z. Ren, H. Choset, and J. Zhang. Representation Granularity Enables Time-Efficient Autonomous Exploration in Large, Complex Worlds. Science Robotics. vol. 8, no. 80, 2023.
C. Cao, H. Zhu, F. Yang, Y. Xia, H. Choset, J. Oh, and J. Zhang. Autonomous Exploration Development Environment and the Planning Algorithms. IEEE Intl. Conf. on Robotics and Automation (ICRA). Philadelphia, PA, May 2022.
J. Zhang, C. Hu, R. Gupta Chadha, and S. Singh. Falco: Fast Likelihood-based Collision Avoidance with Extension to Human-guided Navigation. Journal of Field Robotics. vol. 37, no. 8, pp. 1300–1313, 2020.
J. Zhang and S. Singh. Laser-visual-inertial Odometry and Mapping with High Robustness and Low Drift. Journal of Field Robotics. vol. 35, no. 8, pp. 1242–1264, 2018.