• Rural Digital Europe
• Research dataclose
• Other research productsclose
• 2018-2022close
• Open Access close
• IT close
• Knowmad Institut close

Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.

On 14 January 2021, members of the European Parliament’s (EP) committee on Artificial Intelligence in a Digital Age (AIDA) consulted with experts from the European University Institute (EUI) on topics in the regulation of artificial intelligence (AI). The AIDA committee is also focused on developing a strategy for Europe to survive in a digital world and eventually assume a leadership role in AI. After an introduction in which AIDA committee members outlined their goals and expectations for the role that AI will play in society, the EUI experts provided some general remarks on AI and algorithms, and then presented their original research. Their presentations covered the difficulties of writing laws and regulation for new technologies; economic interactions with AI and algorithmic collusions; and algorithmic content filtering for online platforms. The webinar concluded with statements and questions from the parliamentary groups.This policy brief draws from the experts’ presentations and the questions the Members of the European Parliament (MEP) asked them. It expands on the topics that most interested the MEP and provides further references for policymakers interested in regulating this emerging technology.

Sommario 1. Introduzione 3. Progetti 3.1.1 Fonte di finanziamento 3.1.2 Ambiti di ricerca 3.1.3 Repository 3.1.4 Personale 3. Competenze 3.1.1 Ambiti 3.1.2 ALTRO 3.1.3 COMPETENZE VERTICALI 3.1.4 SUGGERIMENTI 5. CONCLUSIONI ALLEGATI A. PROGETTI PER AMBITI DI RIFERIMENTO B. RICOGNIZIONE CAWI (COMPUTER ASSISTED WEB INTERVIEW) C. SCHEDE INVIATE PER LA COMPILAZIONE ON-LINE

NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine. This is the data to the same-titled Data paper, which can be found at https://doi.org/10.3390/data5010001. Along with auxilary files for the cloudremoval package, and example scripts on how to access chunks of the data. The files contain: python-cloudremoval-aux-data.tar.gz : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal python-example-data-access.html : Example script how to access parts of the data using python R-example-data-access.html : Example script how to access parts of the data using R zenodo_01_original.tar.gz : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (https://doi.org/10.3390/data5010001), and for algorithm at https://doi.org/10.3390/rs5010110. zenodo_02_cloudremoval.tar.gz : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper. The maps are GeoTIFF with integer based values: 0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata Version history: 1.0.0 : initial upload 1.0.1 : changes after revision of Data paper 1.0.2 : added example scripts