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Standard Ontologies and HRI
Published in Paolo Barattini, Vicentini Federico, Gurvinder Singh Virk, Tamás Haidegger, Human–Robot Interaction, 2019
Sandro Rama Fiorini, Abdelghani Chibani, Tamás Haidegger, Joel Luis Carbonera, Craig Schlenoff, Jacek Malec, Edson Prestes, Paulo Gonçalves, S. Veera Ragavan, Howard Li, Hirenkumar Nakawala, Stephen Balakirsky, Sofiane Bouznad, Noauel Ayari, Yacine Amirat
The majority of the entities defined in the standard are specializations of the SUMO concepts and relations. SUMO is a vast top-level ontology developed as part of an IEEE-sponsored effort to create a standard top-level ontology [NP01]. While SUMO never became an actual standard, its flexibility and extensive vocabulary of terms made it one of the main top-ontologies available in the literature. It includes formal theories about processes, spatial relations, temporal relations, information objects and so on. Also, SUMO has been extended along the years to address specific domains, such as engineering and the military.
Human–Robot Collaboration in Industry
Published in Vijaya Kumar Manupati, Goran D. Putnik, Maria Leonilde Rocha Varela, Smart and Sustainable Manufacturing Systems for Industry 4.0, 2023
In the robotic context, the Ontologies for Robotics and Automation Working Group (ORA WG) defines a core ontology for robotics and automation (CORA), which provides concepts common to industrial robots (Prestes et al., 2013). CORA is an extension of SUMO, Suggested Upper Merged Ontology. SUMO is an open-source upper ontology used in several domains (Niles & Pease, 2001). CORA focuses on defining robots, along with any related object. CORAX is an ontology that stands between CORA and SUMO. CORAX represents concepts and relations of common subdomains that are too general to be included in CORA (Fiorini et al., 2015).
Implementing transit signal priority in a connected vehicle environment with and without bus stops
Published in Transportmetrica B: Transport Dynamics, 2019
Kaidi Yang, Monica Menendez, S. Ilgin Guler
In this section, a simulation is used to evaluate the performance of the algorithm. The simulation framework consists of two interacting layers, the real-time traffic simulation layer and the control algorithm layer. The real traffic is simulated by a microscopic simulation package SUMO (Simulation of Urban MObility) (Krajzewicz et al. 2012; SUMO 2015), to evaluate the performance of the algorithm. SUMO is an open-source, highly portable simulation package that has already been used by research works in traffic control and connected vehicle environment (Pandit et al. 2013; Ma, Jin, and Lei 2014; Baiocchi et al. 2015; Le et al. 2015). Flow dynamics in SUMO are based on the car-following model by Krauss, Wagner, and Gawron (1997). The corresponding parameters are calibrated and validated using the trajectories in the Lankershim Boulevard Dataset (8:30–8:45 a.m.) of the Next-Generation Simulation project (NGSim, Alexiadis et al. 2004). The calibrated parameters are maximum speed 60 km/hr, desired acceleration rate 1.7 m/s2, desired deceleration rate 1.7 m/s2, and minimum gap 2.0 m. The real-time vehicle location and speed are simulated by SUMO and sent by its traffic control interface module TraCI to the control layer. In the control layer, the proposed algorithm is coded in Python. The control layer calculates the signal timing using the real traffic information and sends it to the SUMO simulator.
Generating Trips and Assigning Route to a SUMO Network Through the Origin–Destination Matrix: A Case Study of Mobility Routing Model for VANETs
Published in IETE Technical Review, 2022
Tarandeep Kaur Bhatia, Ramkumar Ketti Ramachandran, Robin Doss, Lei Pan
Today, a variety of simulation tools are easily accessible in the VANETs field for simulation work. Among all the available simulation tools, simulation of Urban MObility (SUMO Version 0.30.0) is considered the best option. SUMO [36] is an open-source and easily manageable simulation tool. It is quite simple to understand and expert in managing large road networks. SUMO can be used for the following activities: (1) building a network and assigning routes within a network, (2) developing a traffic light algorithm, and (3) simulating and analyzing traffic within a network through the Origin-Destination (OD) matrix.
Physics-based simulation ontology: an ontology to support modelling and reuse of data for physics-based simulation
Published in Journal of Engineering Design, 2019
Hyunmin Cheong, Adrian Butscher
SUMO, or Suggested Upper Merged Ontologies (Niles and Pease 2001), is essentially an aggregate of various upper-level ontologies, including the first three ontologies mentioned above. It is the largest public upper ontology in terms of its contents (Mascardi, Cordì, and Rosso 2007). Similar to YAMATO, such extensive contents actually make it difficult to be used for developing domain-specific ontologies.