An Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data

Faculty Engineering Year: 2015
Type of Publication: ZU Hosted Pages:
Authors:
Mohammed Nour Abdelgawad Ahmed
Journal: SUUB Staats- und Universitätsbibliothek Bremen Volume:
Keywords : , Intelligent Architecture , Legged Robot Terrain Classification    
Abstract:
In this work, we introduce a novel architecture called ``Intelligent Architecture for Legged Robot Terrain Classification Using Proprioceptive and Exteroceptive Data (iArteC)''. The proposed architecture integrates different terrain charact
    
     
 
       

Author Related Publications

  • Mohammed Nour Abdelgawad Ahmed, "Measurement and Control of the Contact Forces between Walking Robot legs and its Environment", Japanese Society for Terramechanics, 2010 More
  • Mohammed Nour Abdelgawad Ahmed, "Using Industrial Actuators for Rapid Development of Electric Car Applications", WFB Wirtschaftsförderung Bremen, 2014 More
  • Mohammed Nour Abdelgawad Ahmed, "A simulation-based design of extraterrestrial six-legged robot system", IEEE, 2009 More
  • Mohammed Nour Abdelgawad Ahmed, "Modeling of Leg Soil Interaction using Genetic Algorithms", International Society for Terrain-Vehicle Systems, 2011 More
  • Mohammed Nour Abdelgawad Ahmed, "Optimal Genetic-Sliding Mode Control of VSC-HVDC Transmission Systems", Elsevier, 2015 More

Department Related Publications

  • Nesreen I ziedan, "Pattern Recognition-Based Environment Identification for Robust Wireless Devices Positionin", IJACSA, 2013 More
  • Ahmed Mahmoud Abdelrahman Elanany, "Subspace identification with prior steady-state information", IEEE, 2010 More
  • Mohamed Abdelkawy Mohamed Ali Souliman, "Subspace identification with prior steady-state information", IEEE, 2010 More
  • Ibrahiem Elsayed Mohamed Zedan, "Subspace identification with prior steady-state information", IEEE, 2010 More
  • Mohammed Alsayed MohamedAli, "ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment", Elsevier, 2018 More
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