Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments
<|reference_start|>Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments: Autonomous robots navigating in changing environments demand adaptive navigation strategies for safe long-term operation. While many modern control paradigms offer theoretical guarantees, they often assume known extrinsic safety constraints, overlooking challenges when deployed in real-world environments where objects can appear, disappear, and shift over time. In this paper, we present a closed-loop perception-action pipeline that bridges this gap. Our system encodes an online-constructed dense map, along with object-level semantic and consistency estimates into a control barrier function (CBF) to regulate safe regions in the scene. A model predictive controller (MPC) leverages the CBF-based safety constraints to adapt its navigation behaviour, which is particularly crucial when potential scene changes occur. We test the system in simulations and real-world experiments to demonstrate the impact of semantic information and scene change handling on robot behavior, validating the practicality of our approach.<|reference_end|>
@article{qian2024closing,
    title={Closing the Perception-Action Loop for Semantically Safe Navigation in
  Semi-Static Environments},
    author={Jingxing Qian, Siqi Zhou, Nicholas Jianrui Ren, Veronica Chatrath,
  Angela P. Schoellig},
    journal={arXiv preprint arXiv:2404.14546},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14546},
    primaryClass={cs.RO}
}