We introduce a new route-finding problem which considers perception and travel costs simultaneously. Specifically, we consider the problem of finding the shortest tour such that all objects of interest can be detected successfully. To represent a viable detection region for each object, we propose to use an entropy-based viewing score that generates a diameter-bounded region as a viewing neighborhood. We formulate the detection-based trajectory planning problem as a stochastic traveling salesperson problem with neighborhoods and propose a center-visit method that obtains an approximation ratio of O(DmaxDmin) for disjoint regions. For non-disjoint regions, our method -provides a novel finite detour in 3D, which utilizes the region's minimum curvature property. Finally, we show that our method can generate efficient trajectories compared to a baseline method in a photo-realistic simulation environment.

翻译：本文提出了一种同时考虑感知成本与行进成本的新型路径规划问题。具体而言，我们研究如何寻找最短巡视路径，以确保所有目标物体均能被成功检测。为表征每个物体的有效检测区域，我们提出一种基于熵的视域评分方法，该方法可生成直径有界的区域作为视域邻域。我们将基于检测的轨迹规划问题建模为带邻域的随机旅行商问题，并提出一种中心点访问方法，该方法对互不相交区域可获得O(Dmax/Dmin)的近似比。对于非互不相交区域，我们的方法通过利用区域的最小曲率特性，在三维空间中提出了一种创新的有限绕行策略。最后，我们在逼真的仿真环境中通过实验证明，相较于基准方法，本方法能够生成更高效的检测轨迹。