In servitization cases where customers are offered equipment as a service, it is essential to provide efficient on-site service to resolve multiple issues proactively. Autonomous lawn mowing robots operating in unstructured garden environments face issues, such as potholes, which challenge the delivery of a consistent and reliable service. We investigate a rule-based issue detection system using the inertial measurement unit and position sensors to detect terrain irregularities. Thirteen mowing sessions were conducted using a robotic lawnmower, and issues were detected based on predefined thresholds applied to roll, pitch, and z-axis position data. Two experimental approaches were explored: one aggregating all detected anomalies, and another focusing on mowing direction and spatially intersecting anomalies to identify recurring irregularities. The results indicate that direction influences anomaly detection and helps pinpoint terrain issues likely to affect service delivery. This work contributes to the development of terrain-aware service robots aligned with reliability-driven servitization objectives. The results are discussed in terms of how the detection of terrain issues aligns with reliability goals in servitization frameworks.

Transportation-related problems, like road congestion, parking, and pollution, are increasing in most cities. In order to reduce traffic, recent work has proposed methods for vehicle sharing, for example for sharing cabs by grouping "closeby" cab requests and thus minimizing transportation cost and utilizing cab space. However, the methods published so far do not scale to large data volumes, which is necessary to facilitate large-scale collective transportation systems, e.g., ride-sharing systems for large cities. This paper presents highly scalable trip grouping algorithms, which generalize previous techniques and support input rates that can be orders of magnitude larger. The following three contributions make the grouping algorithms scalable. First, the basic grouping algorithm is expressed as a continuous stream query in a data stream management system to allow for a very large flow of requests. Second, following the divide-and-conquer paradigm, four space-partitioning policies for dividing the input data stream into sub-streams are developed and implemented using continuous stream queries. Third, using the partitioning policies, parallel implementations of the grouping algorithm in a parallel computing environment are described. Extensive experimental results show that the parallel implementation using simple adaptive partitioning methods can achieve speed-ups of several orders of magnitude without significantly degrading the quality of the grouping.

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