For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizations, namely GATv2, has potential pitfalls that hinder an optimal parameter learning. Especially for small and sparse graph structures a proper optimization is problematic. To surpass limitations, this work proposes architectural modifications of GATv2. In controlled experiments, it is shown that the proposed model adaptions improve prediction performance in a node-level regression task and make it more robust to parameter initialization. This work aims for a better understanding of the attention mechanism and analyzes its interpretability of identifying causal importance.

We present the first realization of an assembly sequence planning framework for large-scale and complex 3D real-world CAD scenarios. Other than in academic benchmark data sets, in our scenario each assembled part is allowed to contain flexible fastening elements and the number of assembled parts is quite high. With our framework we are able to derive a meaningful assembly priority graph for the parts. Our framework divides the disassembly motion of each part into a NEAR- and a subsequent FAR planning phase and uses existing specialized motion planners for each phase. To reduce the number of unsuccessful motion planning requests we use a general Voronoi diagram graph and a novel collision perceiving method which significantly speed up our framework. At the end, we create an assembly priority graph to indicate which parts must be disassembled before others. In our experiments, we show that our framework is the first one which is able to generate a priority graph for a representative data set from the automotive industry. Moreover, the reported disassembly motions for the individual parts are shorter and can be computed faster than with other state-of-the-art frameworks.

One major challenge in Assembly Sequence Planning (ASP) for complex real-world CAD-scenarios is to find an appropriate disassembly path for each assembled part. Complex real-world scenes are characterized by a large installation space. There each part has many different possible disassembly paths that differ in length and clearance. However, due to tight packing in the installation space, these paths can contain narrow passages. Therefore a motion planner is needed that is able to globally search for a reasonable path and to locally overcome narrow passages. Moreover, since motion planning requests are executed in the ASP context over and over again for many parts, both for those that can be disassembled in the next step and for those that cannot be yet, the motion planner has to be reliably fast.We present a new rigid body motion planner, called Expansive Voronoi Tree (EVT), which is optimized for complex ASP scenarios. The EVT estimates a globally reasonable path using a General Voronoi Diagram of the complete scene. With a novel EST-based sampling strategy, which is the contribution of this paper, it then locally explores the environment along the estimated path. The EVT automatically adapts to different clearance situations. It passes wide environments quickly and samples densely at narrow passages.We compare our EVT to state of the art motion planners which use different sampling strategies on a real-world data set consisting of a large subset of a car. The experiments show that the EVT is reliably many times faster and delivers shorter paths.

One major challenge in Assembly Sequence Planning (ASP) for complex real-world CAD-scenarios is to find appropriate disassembly paths for all assembled parts. Such a path places demands on its length and clearance. In the past, it became apparent that planning the disassembly path based on the (approximate) General Voronoi Diagram (GVD) is a good approach to achieve these requirements. But for complex real-world data, every known solution for computing the GVD is either too slow or very memory consuming, even if only approximating the GVD.We present a new approach for computing the approximate GVD and demonstrate its practicability using a representative vehicle data set. We can calculate an approximation of the GVD within minutes and meet the accuracy requirement of some few millimeters for the subsequent path planning. This is achieved by voxelizing the surface with a common error-bounded GPU render approach. We then use an error-bounded wavefront propagation technique and combine it with a novel hash table-based data structure, the so-called Voronoi Voxel History (VVH). On top of the GVD, we present a novel approach for the creation of a General Voronoi Diagram Graph (GVDG) that leads to an extensive roadmap. For the later motion planning task this roadmap can be used to suggest appropriate disassembly paths.