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  • 1. Herdt, Andrei
    et al.
    Diedam, Holger
    Wieber, Pierre-Brice
    Dimitrov, Dimitar
    Örebro universitet, Akademin för naturvetenskap och teknik.
    Mombaur, Katja
    Diehl, Moritz
    Online walking motion generation with automatic footstep placement2010Ingår i: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 24, nr 5-6, s. 719-737Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The goal of this paper is to demonstrate the capacity of model predictive control (MPC) to generate stable walking motions without the use of predefined footsteps. Building up on well-known MPC schemes for walking motion generation, we show that a minimal modification of these schemes allows designing an online walking motion generator that can track a given reference speed of the robot and decide automatically the footstep placement. Simulation results are proposed on the HRP-2 humanoid robot, showing a significant improvement over previous approaches.

  • 2.
    Kamarudin, Kamarulzaman
    et al.
    Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia; School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Arau, Malaysia.
    Shakaff, Ali Yeon Md
    Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia; School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Arau, Malaysia.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Mamduh, Syed Muhammad
    Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia.
    Zakaria, Ammar
    Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia; School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Arau, Malaysia.
    Visvanathan, Retnam
    Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia.
    Yeon, Ahmad Shakaff Ali
    Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia.
    Kamarudin, Latifah Munirah
    Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, Arau, Malaysia.
    Integrating SLAM and gas distribution mapping (SLAM-GDM) for real-time gas source localization2018Ingår i: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 32, nr 17, s. 903-917Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Gas distribution mapping (GDM) learns models of the spatial distribution of gas concentrations across 2D/3D environments, among others, for the purpose of localizing gas sources. GDM requires run-time robot positioning in order to associate measurements with locations in a global coordinate frame. Most approaches assume that the robot has perfect knowledge about its position, which does not necessarily hold in realistic scenarios. We argue that the simultaneous localization and mapping (SLAM) algorithm should be used together with GDM to allow operation in an unknown environment. This paper proposes an SLAM-GDM approach that combines Hector SLAM and Kernel DM+V through a map merging technique. We argue that Hector SLAM is suitable for the SLAM-GDM approach since it does not perform loop closure or global corrections, which in turn would require to re-compute the gas distribution map. Real-time experiments were conducted in an environment with single and multiple gas sources. The results showed that the predictions of gas source location in all trials were often correct to around 0.5-1.5 m for the large indoor area being tested. The results also verified that the proposed SLAM-GDM approach and the designed system were able to achieve real-time operation.

  • 3.
    Lilienthal, Achim J.
    et al.
    W.-Schickard-Inst. for Comp. Science, University of Tübingen, Tübingen, Germany.
    Duckett, Tom
    Örebro universitet, Institutionen för teknik.
    Experimental analysis of gas-sensitive Braitenberg vehicles2004Ingår i: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 18, nr 8, s. 817-834Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This article addresses the problem of localising a static gas source in an indoor environment by a mobile robot. In contrast to previous works, the environment is not artificially ventilated to produce a strong unidirectional airflow. Here, the dominant transport mechanisms of gas molecules are turbulence and convection flow rather than diffusion, which results in a patchy, chaotically fluctuating gas distribution. Two Braitenberg-type strategies (positive and negative tropotaxis) based on the instantaneously measured spatial concentration gradient were investigated. Both strategies were shown to be of potential use for gas source localisation. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localisation strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions

  • 4.
    Neumann, Patrick
    et al.
    Federal Institute for Materials Research and Testing (BAM), Berlin, Germany.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bartholmai, Matthias
    Federal Institute for Materials Research and Testing (BAM), Berlin, Germany.
    Schiller, Jochen H.
    Institute of Computer Science, Freie Universität, Berlin, Germany.
    Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms2013Ingår i: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, ISSN 0169-1864, Vol. 27, nr 9, s. 725-738Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion,the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposesan integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-basedplume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitivemicro-drone. We compare the performance of the proposed system in simulations and real-world experiments againsttwo commonly used tracking algorithms adapted for aerial exploration missions.

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