让机器人在视线受阻时自动避障，边探索边安全前进

Navigating complex, cluttered, and unstructured environments that are a priori unknown presents significant challenges for autonomous ground vehicles, particularly when operating with a limited field of view(FOV) resulting in frequent occlusion and unobserved space. This paper introduces a novel visibility-aware model predictive path integral framework(VA-MPPI). Formulated as a dual control problem where perceptual uncertainties and control decisions are intertwined, it reasons over perception uncertainty evolution within a unified planning and control pipeline. Unlike traditional methods that rely on explicit uncertainty objectives, the VA-MPPI controller implicitly balances exploration and exploitation, reducing uncertainty only when system performance would be increased. The VA-MPPI framework is evaluated in simulation against deterministic and prescient controllers across multiple scenarios, including a cluttered urban alleyway and an occluded off-road environment. The results demonstrate that VA-MPPI significantly improves safety by reducing collision with unseen obstacles while maintaining competitive performance. For example, in the off-road scenario with 400 control samples, the VA-MPPI controller achieved a success rate of 84%, compared to only 8% for the deterministic controller, with all VA-MPPI failures arising from unmet stopping criteria rather than collisions. Furthermore, the controller implicitly avoids unobserved space, improving safety without explicit directives. The proposed framework highlights the potential for robust, visibility-aware navigation in unstructured and occluded environments, paving the way for future advancements in autonomous ground vehicle systems.