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  • 1.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar Nikolaev
    INRIA - Grenoble, Meylan, France.
    Driankov, Dimiter
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saarinen, Jari Pekka
    Örebro University, School of Science and Technology. Aalto University, Espo, Finland .
    Sherikov, Aleksander
    Centre de recherche Grenoble Rhône-Alpes, Grenoble, France .
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Autonomous transport vehicles: where we are and what is missing2015In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 22, no 1, p. 64-75Article in journal (Refereed)
    Abstract [en]

    In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies.

  • 2.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Drive the Drive: From Discrete Motion Plans to Smooth Drivable Trajectories2014In: Robotics, E-ISSN 2218-6581, Vol. 3, no 4, p. 400-416Article in journal (Refereed)
    Abstract [en]

    Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation).

  • 3.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology. SCANIA AB, Södertälje, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Fast, continuous state path smoothing to improve navigation accuracy2015In: IEEE International Conference on Robotics and Automation (ICRA), 2015, IEEE Computer Society, 2015, p. 662-669Conference paper (Refereed)
    Abstract [en]

    Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation.

  • 4.
    Saarinen, Jari
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Ala-Luhtala, Juha
    Aalto University of Technology, Aalto, Finland.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Normal distributions transform occupancy maps: application to large-scale online 3D mapping2013In: IEEE International Conference on Robotics and Automation, New York: IEEE conference proceedings, 2013, p. 2233-2238Conference paper (Refereed)
    Abstract [en]

    Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper proposes to address the challenges of online real-world mapping by building upon previous work on compact spatial representation and formulating a novel 3D mapping approach — the Normal Distributions Transform Occupancy Map (NDT-OM). The presented algorithm enables accurate real-time 3D mapping in large-scale dynamic nvironments employing a recursive update strategy. In addition, the proposed approach can seamlessly provide maps at multiple resolutions allowing for fast utilization in high-level functions such as localization or path planning. Compared to previous approaches that use the NDT representation, the proposed NDT-OM formulates an exact and efficient recursive update formulation and models the full occupancy of the map.

  • 5.
    Saarinen, Jari
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    3D normal distributions transform occupancy maps: an efficient representation for mapping in dynamic environments2013In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 32, no 14, p. 1627-1644Article in journal (Refereed)
    Abstract [en]

    In order to enable long-term operation of autonomous vehicles in industrial environments numerous challenges need to be addressed. A basic requirement for many applications is the creation and maintenance of consistent 3D world models. This article proposes a novel 3D spatial representation for online real-world mapping, building upon two known representations: normal distributions transform (NDT) maps and occupancy grid maps. The proposed normal distributions transform occupancy map (NDT-OM) combines the advantages of both representations; compactness of NDT maps and robustness of occupancy maps. One key contribution in this article is that we formulate an exact recursive updates for NDT-OMs. We show that the recursive update equations provide natural support for multi-resolution maps. Next, we describe a modification of the recursive update equations that allows adaptation in dynamic environments. As a second key contribution we introduce NDT-OMs and formulate the occupancy update equations that allow to build consistent maps in dynamic environments. The update of the occupancy values are based on an efficient probabilistic sensor model that is specially formulated for NDT-OMs. In several experiments with a total of 17 hours of data from a milk factory we demonstrate that NDT-OMs enable real-time performance in large-scale, long-term industrial setups.

  • 6.
    Saarinen, Jari
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Normal distributions transform monte-carlo localization (NDT-MCL)2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, p. 382-389Conference paper (Refereed)
  • 7.
    Saarinen, Jari
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, p. 4694-4701Conference paper (Refereed)
  • 8.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Normal distributions transform occupancy map fusion: simultaneous mapping and tracking in large scale dynamic environments2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, p. 4702-4708Conference paper (Refereed)
  • 9.
    Valencia, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Vallvé, Joan
    CSIC-UPC, Barcelona,Spain.
    Andrade-Cetto, Juan
    CSIC-UPC, Barcelona, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Localization in highly dynamic environments using dual-timescale NDT-MCL2014In: 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE Robotics and Automation Society, 2014, p. 3956-3962Conference paper (Refereed)
    Abstract [en]

    Industrial environments are rarely static and oftentheir configuration is continuously changing due to the materialtransfer flow. This is a major challenge for infrastructure freelocalization systems. In this paper we address this challengeby introducing a localization approach that uses a dualtimescaleapproach. The proposed approach - Dual-TimescaleNormal Distributions Transform Monte Carlo Localization (DTNDT-MCL) - is a particle filter based localization method,which simultaneously keeps track of the pose using an aprioriknown static map and a short-term map. The short-termmap is continuously updated and uses Normal DistributionsTransform Occupancy maps to maintain the current state ofthe environment. A key novelty of this approach is that it doesnot have to select an entire timescale map but rather use thebest timescale locally. The approach has real-time performanceand is evaluated using three datasets with increasing levels ofdynamics. We compare our approach against previously proposedNDT-MCL and commonly used SLAM algorithms andshow that DT-NDT-MCL outperforms competing algorithmswith regards to accuracy in all three test cases.

1 - 9 of 9
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  • ieee
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  • en-US
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