A new pressure sensor array, positioned on the bottom plate of a standard torsional rheometer is presented. It is built from a unique piezo-capacitive polymeric foam, and consists of twentyfive capacitive pressure sensors (of surface 4.5×4.5 mm 2 each) built together in a 5×5 regular array. The sensor array is used to obtain a local mapping of the normal stresses in complex fluids, which dramatically extends the capability of the rheometer. We demonstrate this with three examples. First, the pressure profile is reconstructed in a polymer solution, which enable the simultaneous measurement of the first and the second normal stress differences N 1 and N 2 , with a precision of 2 Pa. In a second part, we show that negative normal stresses can also be detected.Finally, we focus on the normal stress fluctuations that extend both spatially and temporally in a shear-thickening suspension of cornstarch particles. We evidence the presence of local a unique heterogeneity rotating very regularly. In addition to their low-cost and high versatility, the sensors show here their potential to finely characterize the normal stresses in viscosimetric flows.

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Challenges inherent to autonomous wintertime navigation in forests include lack of a reliable Global Navigation Satellite System (GNSS) signal, low feature contrast, high illumination variations, and changing environment. This type of off-road environment is an extreme case of situations autonomous cars could encounter in northern regions. Thus, it is important to understand the impact of this harsh environment on autonomous navigation systems. To this end, we present a field report analyzing teach-and-repeat navigation in a subarctic forest while subject to fluctuating weather, including light and heavy snow, rain, and drizzle. First, we describe the system, which relies on point cloud registration to localize a mobile robot through a boreal forest, while simultaneously building a map. We experimentally evaluate this system in over 18.8km of autonomous navigation in the teach-and-repeat mode. Over 14 repeat runs, only four manual interventions were required, three of which were due to localization failure and another one caused by battery power outage. We show that dense vegetation perturbs the GNSS signal, rendering it unsuitable for navigation in forest trails. Furthermore, we highlight the increased uncertainty related to localizing using point cloud registration in forest trails. We demonstrate that it is not snow precipitation, but snow accumulation, that affects our system’s ability to localize within the environment. Finally, we expose some challenges and lessons learned from our field campaign to support better experimental work in winter conditions. Our dataset is available online.

Wood logs picking is a challenging task to automate. Indeed, logs usually come in cluttered configurations, randomly orientated and overlapping. Recent work on log picking automation usually assume that the logs' pose is known, with little consideration given to the actual perception problem. In this paper, we squarely address the latter, using a datadriven approach. First, we introduce a novel dataset, named TimberSeg 1.0, that is densely annotated, i.e., that includes both bounding boxes and pixel-level mask annotations for logs. This dataset comprises 220 images with 2500 individually segmented logs. Using our dataset, we then compare three neural network architectures on the task of individual logs detection and segmentation; two region-based methods and one attention-based method. Unsurprisingly, our results show that axis-aligned proposals, failing to take into account the directional nature of logs, underperform with 19.03 mAP. A rotation-aware proposal method significantly improve results to 31.83 mAP. More interestingly, a Transformer-based approach, without any inductive bias on rotations, outperformed the two others, achieving a mAP of 57.53 on our dataset. Our use case demonstrates the limitations of region-based approaches for cluttered, elongated objects. It also highlights the potential of attention-based methods on this specific task, as they work directly at the pixel-level. These encouraging results indicate that such a perception system could be used to assist the operators on the short-term, or to fully automate log picking operations in the future.