Safe and efficient trajectory planning in unknown, cluttered 3D environments constitutes a critical bottleneck for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications. This challenge is further exacerbated by the limited field-of-view (FOV) and sensing range of onboard sensors. Many existing methods either make simplistic assumptions about unexplored space or rely on conservative heuristics such as speed limits or fixed perception patterns, reducing efficiency and generalizing poorly across different sensor types. In this work, we propose a novel planning framework that directly integrates active perception into trajectory optimization, thereby improving safety while preserving efficiency. The perception constraints are derived from the UAV's dynamic model and formulated in the sensor coordinate frame, which enables precise handling of FOV geometry. The velocity-triggered activation mechanism enables the planner to balance perception and motion efficiency. We introduce an active perception sub-trajectory segment with parametric start-time optimization, mitigating collision risks from late obstacle detection. Our formulation enables active perception during arbitrary 3D maneuvers, extending beyond prior methods designed mainly for horizontal motion. All constraints and penalties are incorporated into a differentiable optimization problem, so the planner requires only a simple front-end global path for guidance, rather than a computationally expensive perception-aware path generator. Extensive simulations and real-world experiments demonstrate robust performance across diverse unknown environments with varying sensor configurations.

翻译：在未知且杂乱的3D环境中实现安全高效的轨迹规划是无人机在真实世界应用中部署的关键瓶颈。机载传感器有限的视场和感知范围进一步加剧了这一挑战。现有方法要么对未探索空间做出简化假设，要么依赖保守启发式策略（如速度限制或固定感知模式），导致效率降低且在不同传感器类型间泛化能力差。本文提出一种新型规划框架，将主动感知直接融入轨迹优化，在保持效率的同时提升安全性。感知约束基于无人机动力学模型推导并建立于传感器坐标系下，可实现视场几何结构的精确处理。速度触发激活机制使规划器能够平衡感知与运动效率。我们引入带参数化起始时间优化的主动感知子轨迹段，以降低因障碍物检测延迟引发的碰撞风险。该公式支持任意三维机动中的主动感知，突破了先前主要面向水平运动方法的局限。所有约束与惩罚项均整合至可微优化问题中，因此规划器仅需简单的前端全局路径作为引导，而无需计算成本高昂的感知感知路径生成器。大量仿真与真实世界实验表明，该方法在不同传感器配置的未知环境中均展现出稳健性能。