Autonomous Exploration
Autonomous exploration focuses on enabling robots to independently map and navigate unknown environments, optimizing efficiency and completeness of coverage. Current research emphasizes efficient mapping techniques using various sensor modalities (LiDAR, RGB-D cameras), incorporating semantic information for improved understanding, and leveraging advanced algorithms like deep reinforcement learning, active inference, and graph-based methods for planning optimal exploration trajectories. This field is crucial for advancing robotics in diverse applications, including search and rescue, planetary exploration, and industrial inspection, by enabling robots to operate effectively in unstructured and dynamic settings.
Papers
Flexible Supervised Autonomy for Exploration in Subterranean Environments
Harel Biggie, Eugene R. Rush, Danny G. Riley, Shakeeb Ahmad, Michael T. Ohradzansky, Kyle Harlow, Michael J. Miles, Daniel Torres, Steve McGuire, Eric W. Frew, Christoffer Heckman, J. Sean Humbert
Bayesian Generalized Kernel Inference for Exploration of Autonomous Robots
Yang Xu, Ronghao Zheng, Senlin Zhang, Meiqin Liu