An important open problem in robotic planning is the autonomous generation of
3D inspection paths -- that is, planning the best path to move a robot along in
order to inspect a target structure. We recently suggested a new method for
planning paths allowing the inspection of complex 3D structures, given a
triangular mesh model of the structure. The method differs from previous
approaches in its emphasis on generating and considering also plans that result
in imperfect coverage of the inspection target. In many practical tasks, one
would accept imperfections in coverage if this results in a substantially more
energy efficient inspection path. The key idea is using a multiobjective
evolutionary algorithm to optimize the energy usage and coverage of inspection
plans simultaneously - and the result is a set of plans exploring the different
ways to balance the two objectives. We here test our method on a set of
inspection targets with large variation in size and complexity, and compare its
performance with two state-of-the-art methods for complete coverage path
planning. The results strengthen our confidence in the ability of our method to
generate good inspection plans for different types of targets. The method's
advantage is most clearly seen for real-world inspection targets, since
traditional complete coverage methods have no good way of generating plans for
structures with hidden parts. Multiobjective evolution, by optimizing energy
usage and coverage together ensures a good balance between the two - both when
100% coverage is feasible, and when large parts of the object are hidden.

使用多目标演化算法，在保证能量使用和覆盖率平衡的同时，规划机器人的路径，实现复杂三维结构的检测。

多目标覆盖路径规划：实现复杂实际结构的自动化检测

Multiobjective Coverage Path Planning: Enabling Automated Inspection of  Complex, Real-World Structures

We present a novel deep neural network architecture for unsupervised subspace
clustering. This architecture is built upon deep auto-encoders, which
non-linearly map the input data into a latent space. Our key idea is to
introduce a novel self-expressive layer between the encoder and the decoder to
mimic the "self-expressiveness" property that has proven effective in
traditional subspace clustering. Being differentiable, our new self-expressive
layer provides a simple but effective way to learn pairwise affinities between
all data points through a standard back-propagation procedure. Being nonlinear,
our neural-network based method is able to cluster data points having complex
(often nonlinear) structures. We further propose pre-training and fine-tuning
strategies that let us effectively learn the parameters of our subspace
clustering networks. Our experiments show that the proposed method
significantly outperforms the state-of-the-art unsupervised subspace clustering
methods.

本文提出了一种基于深度自编码器的无监督子空间聚类新颖深度神经网络架构，其中引入了一种自相似层，以模拟传统子空间聚类方法中的 “自相似” 属性。该方法能够在非线性方式下对具有复杂结构的数据点进行聚类，并且在实验中表现出优于现有方法的性能。