Calculating embodied carbon for reused structural components with laser scanning

The global warming potential (GWP) of reused building elements can be evaluated based on two variables: structural material quantity (SMQ) and embodied carbon coefficient (ECC). The volume of the SMQ can often be unknown, however, and it is not clear how to best estimate the ECC of a reused element. This paper illustrates a method for extracting the volume of reused metal structural elements to calculate their GWP in buildings that lack documentation. The authors use laser scanning and voxelization to extract the volume and a method based on the Swiss Society of Engineers and Architects (SIA) 2032 norms for calculating the GWP of reused materials. The reality capture method is accurate enough to approximate structural material volume, although it requires exposed structures. The results are important for building managers to understand the relative environmental impact savings from reused versus new building elements

HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

Autonomous robots can benefit greatly from human-provided semantic characterizations of uncertain task environments and states. However, the development of integrated strategies which let robots model, communicate, and act on such 'soft data' remains challenging. Here, the Human Assisted Robotic Planning and Sensing (HARPS) framework is presented for active semantic sensing and planning in human-robot teams to address these gaps by formally combining the benefits of online sampling-based POMDP policies, multimodal semantic interaction, and Bayesian data fusion. This approach lets humans opportunistically impose model structure and extend the range of semantic soft data in uncertain environments by sketching and labeling arbitrary landmarks across the environment. Dynamic updating of the environment model while during search allows robotic agents to actively query humans for novel and relevant semantic data, thereby improving beliefs of unknown environments and states for improved online planning. Simulations of a UAV-enabled target search application in a large-scale partially structured environment show significant improvements in time and belief state estimates required for interception versus conventional planning based solely on robotic sensing. Human subject studies in the same environment (n = 36) demonstrate an average doubling in dynamic target capture rate compared to the lone robot case, and highlight the robustness of active probabilistic reasoning and semantic sensing over a range of user characteristics and interaction modalities

Towards a Generic Solution for Inspection of Industrial Sites

Autonomous robotic inspection of industrial sites offers a huge potential with respect to increasing human safety and operational efficiency. The present paper provides an insight into the approach taken by team LIO during the ARGOS Challenge. In this international competition, the legged robot ANYmal was equipped with a sensor head to perform visual, acoustic, and thermal inspection on an oil and gas site. The robot was able to autonomously navigate on the outdoor industrial facilty using rotating line-LIDAR sensors for localization and terrain mapping. Thanks to the superior mobility of legged robots, ANYmal can omni-directionally move with statically and dynamically stable gaits while overcoming large obstacles and stairs. Moreover, the versatile machine can adapt its posture for inspection. The paper additionally provides insight into the methods applied for visual inspection of pressure gauges and concludes with some insight into the general learnings from the ARGOS Challenge