Marvin Rüdt

Marvin Rüdt, Constantin Enke, Kai Furmans

Efficient routing of mobile robot fleets requires roadmaps with high redundancy, short path lengths, and sufficient node and edge clearance for conflict-free operation. Existing grid-based methods sacrifice geometric fidelity and impose Manhattan-distance path length constraints, whereas existing continuous-space methods neglect minimum distance constraints and transport demand. This paper proposes a continuous-space roadmap generation method that addresses this gap by placing nodes at convex corner points of the free space and at station interaction points, discretizing free space via local grid expansion, enforcing minimum inter-node and node-edge distance constraints derived from robot dimensions, and applying transport demand-driven K-shortest path pruning. The method is evaluated across three intralogistics environments using two multi-agent pickup and delivery (MAPD) solvers against three baselines: a reaction-diffusion sampling method (GSRM), an 8-connected grid, and random sampling. Under Priority Inheritance with Backtracking (PIBT), the proposed method outperforms GSRM by 1.2-23.4 % at maximum fleet size, the grid by at least 9.1 %, and random sampling by more than 10.4 % across all environments, with a space-time A* solver confirming these results. It further attains near-optimal normalized path lengths of 1.03-1.05 and the highest inter-station connectivity at comparable roadmap complexity.

Marvin Rüdt, Hao Pang, Constantin Enke et al.

Autonomous mobile robots operating in intralogistics environments rely on geometric maps for localization and navigation, but lack semantic understanding of objects and their contextual properties. We present a contextual semantic mapping pipeline that combines SLAM-based geometric mapping, SAM-based instance segmentation, instance clustering, and VLM multi-view reasoning to produce a contextual semantic map representation encoding geometric structure, object class, and object movability. By aggregating observations across multiple viewpoints and querying a VLM in a zero-shot, open-vocabulary setting, the pipeline infers contextual object properties--here demonstrated through movability--without requiring task-specific training or predefined object categories. We evaluate three VLMs under two prompting strategies and conduct a component-wise analysis of the pipeline. The proposed pipeline achieves 98.93 % mIoU for semantic classification and 89.17 % mAcc for object movability estimation. Component analysis identifies VLM reasoning as the primary bottleneck for contextual understanding and instance clustering as the main limitation for panoptic performance. The resulting semantic map supports context-aware filtering and robust navigation in dynamic intralogistics environments.