Height Change Feature Based Free Space Detection

In the context of autonomous forklifts, ensuring non-collision during travel, pick, and place operations is crucial. To accomplish this, the forklift must be able to detect and locate areas of free space and potential obstacles in its environment. However, this is particularly challenging in highly dynamic environments, such as factory sites and production halls, due to numerous industrial trucks and workers moving throughout the area. In this paper, we present a novel method for free space detection, which consists of the following steps. We introduce a novel technique for surface normal estimation relying on spherical projected LiDAR data. Subsequently, we employ the estimated surface normals to detect free space. The presented method is a heuristic approach that does not require labeling and can ensure real-time application due to high processing speed. The effectiveness of the proposed method is demonstrated through its application to a real-world dataset obtained on a factory site both indoors and outdoors, and its evaluation on the Semantic KITTI dataset [2]. We achieved a mean Intersection over Union (mIoU) score of 50.90 % on the benchmark dataset, with a processing speed of 105 Hz. In addition, we evaluated our approach on our factory site dataset. Our method achieved a mIoU score of 63.30 % at 54 H

Poster: How to Raise a Robot - Beyond Access Control Constraints in Assistive Humanoid Robots

Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities. However, while these robots need a certain degree of autonomy to learn and explore, they also should respect various constraints, for access control and beyond. We explore incorporating privacy and security constraints (Activity-Centric Access Control and Deep Learning Based Access Control) with robot task planning approaches (classical symbolic planning and end-to-end learning-based planning). We report preliminary results on their respective trade-offs and conclude that a hybrid approach will most likely be the method of choice

How to Raise a Robot - A Case for Neuro-Symbolic AI in Constrained Task Planning for Humanoid Assistive Robots

Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities. However, while these robots need a certain degree of autonomy to learn and explore, they also should respect various constraints, for access control and beyond. We explore the novel field of incorporating privacy, security, and access control constraints with robot task planning approaches. We report preliminary results on the classical symbolic approach, deep-learned neural networks, and modern ideas using large language models as knowledge base. From analyzing their trade-offs, we conclude that a hybrid approach is necessary, and thereby present a new use case for the emerging field of neuro-symbolic artificial intelligence

Joint inversion estimate of regional glacial isostatic adjustment in Antarctica considering a lateral varying Earth structure (ESA STSE Project REGINA)

A major uncertainty in determining the mass balance of the Antarctic ice sheet from measurements of satellite gravimetry, and to a lesser extent satellite altimetry, is the poorly known correction for the ongoing deformation of the solid Earth caused by glacial isostatic adjustment (GIA). Although much progress has been made in consistently modelling the ice-sheet evolution throughout the last glacial cycle, as well as the induced bedrock deformation caused by these load changes, forward models of GIA remain ambiguous due to the lack of observational constraints on the ice sheet's past extent and thickness and mantle rheology beneath the continent. As an alternative to forward modelling GIA, we estimate GIA from multiple space-geodetic observations: GRACE, Envisat/ICESat and GPS. Making use of the different sensitivities of the respective satellite observations to current and past surface mass (ice mass) change and solid Earth processes, we estimate GIA based on viscoelastic response functions to disc load forcing. We calculate and distribute the viscoelastic response functions according to estimates of the variability of lithosphere thickness and mantle viscosity in Antarctica. We compare our GIA estimate with published GIA corrections and evaluate its impact in determining the ice mass balance in Antarctica from GRACE and satellite altimetry. Particular focus is applied to the Amundsen Sea Sector in West Antarctica, where uplift rates of several cm/yr have been measured by GPS. We show that most of this uplift is caused by the rapid viscoelastic response to recent ice-load changes, enabled by the presence of a low-viscosity upper mantle in West Antarctica. This paper presents the second and final contribution summarizing the work carried out within a European Space Agency funded study, REGINA, (www.regina-science.eu)

Acceleration of dynamic ice loss in Antarctica from satellite gravimetry

The dynamic stability of the Antarctic Ice Sheet is one of the largest uncertainties in projections of future global sea-level rise. Essential for improving projections of the ice sheet evolution is the understanding of the ongoing trends and accelerations of mass loss in the context of ice dynamics. Here, we examine accelerations of mass change of the Antarctic Ice Sheet from 2002 to 2020 using data from the GRACE (Gravity Recovery and Climate Experiment; 2002–2017) and its follow-on GRACE-FO (2018-present) satellite missions. By subtracting estimates of net snow accumulation provided by re-analysis data and regional climate models from GRACE/GRACE-FO mass changes, we isolate variations in ice-dynamic discharge and compare them to direct measurements based on the remote sensing of the surface-ice velocity (2002–2017). We show that variations in the GRACE/GRACE-FO time series are modulated by variations in regional snow accumulation caused by large-scale atmospheric circulation. We show for the first time that, after removal of these surface effects, accelerations of ice-dynamic discharge from GRACE/GRACE-FO agree well with those independently derived from surface-ice velocities. For 2002–2020, we recover a discharge acceleration of -5.3 ± 2.2 Gt yr−2 for the entire ice sheet; these increasing losses originate mainly in the Amundsen and Bellingshausen Sea Embayment regions (68%), with additional significant contributions from Dronning Maud Land (18%) and the Filchner-Ronne Ice Shelf region (13%). Under the assumption that the recovered rates and accelerations of mass loss persisted independent of any external forcing, Antarctica would contribute 7.6 ± 2.9 cm to global mean sea-level rise by the year 2100, more than two times the amount of 2.9 ± 0.6 cm obtained by linear extrapolation of current GRACE/GRACE-FO mass loss trends

Quantifying the Potential of Photonic Cooling to Improve Urban Microclimate

The observed warming trend in regional climate is expected to continue in the future, aggravating urban heat load as extreme temperatures are amplified in cities due to the urban heat island (UHI) effect. Beside causing negative health effects and reducing human comfort, this development results in an increase in urban air conditioning (AC) usage, again negatively influencing the outdoor urban microclimate due to AC waste heat emission. As cities are continiously growing (the population of e.g. Vienna increased more than 10% over the past 10 years), more and more people are affected by this additional anthropogenic heating of the urban canyon. The Viennese trend away from individual motorized traffic such as cars and towards the use of public transport, walking and cycling further leaves increased numbers of inhabitants directly exposed to excessive heat loads, highlighting the need for innovative solutions to counteract this problem. The exploratory project ‘Photonic Cooling’, funded by the Austrian Research Promotion Agency through the ‘City of the Future’ program, aims at evaluating the potential of practical and cost-effective photonic cooling techniques for the cooling of buildings. The use of the photonic cooling technology instead of conventional AC systems minimizes anthropogenic heat emissions resulting from building cooling, hence minimizing the UHI development due to AC heat release and improving the quality of life of the urban population as a result. This paper focusses on the quantification of the potential of photonic cooling to improve the urban microclimate using Vienna as a case study. To estimate the future development of the UHI, the resulting changes in cooling demand and its effect on urban temperatures, a modelling approach is used. Simulations with the MUKLIMO_3 urban climate model are performed for the city of Vienna to determine changes in urban temperature for the 2021-2050 period relative to the 1971-2000 period. These results are then used as input for an empirical model to determine future cooling demand in terms of AC electricity use in buildings. Based on existing studies for other cities a relation between AC heat release and city temperature increase is established. Combining this with the modelled future cooling demand quantifies the influence from conventional AC systems on the urban microclimate, illustrating the benefit of using passive photonic cooling techniques to cover cooling demands instead