1,554 research outputs found
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Underwater intervention robotics: An outline of the Italian national project Maris
The Italian national project MARIS (Marine Robotics for Interventions) pursues the strategic objective of studying, developing, and integrating technologies and methodologies to enable the development of autonomous underwater robotic systems employable for intervention activities. These activities are becoming progressively more typical for the underwater offshore industry, for search-and-rescue operations, and for underwater scientific missions. Within such an ambitious objective, the project consortium also intends to demonstrate the achievable operational capabilities at a proof-of-concept level by integrating the results with prototype experimental systems
Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds
Audio-visual navigation combines sight and hearing to navigate to a
sound-emitting source in an unmapped environment. While recent approaches have
demonstrated the benefits of audio input to detect and find the goal, they
focus on clean and static sound sources and struggle to generalize to unheard
sounds. In this work, we propose the novel dynamic audio-visual navigation
benchmark which requires catching a moving sound source in an environment with
noisy and distracting sounds, posing a range of new challenges. We introduce a
reinforcement learning approach that learns a robust navigation policy for
these complex settings. To achieve this, we propose an architecture that fuses
audio-visual information in the spatial feature space to learn correlations of
geometric information inherent in both local maps and audio signals. We
demonstrate that our approach consistently outperforms the current
state-of-the-art by a large margin across all tasks of moving sounds, unheard
sounds, and noisy environments, on two challenging 3D scanned real-world
environments, namely Matterport3D and Replica. The benchmark is available at
http://dav-nav.cs.uni-freiburg.de
Multi Agent Systems in Logistics: A Literature and State-of-the-art Review
Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems
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