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  • 1.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Mattias
    MRO Lab of the AASS Research Centre, Örebro University, Örebro, Sweden.
    Kubelka, Vladimír
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    TBV Radar SLAM - Trust but Verify Loop Candidates2023In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3613-3620Article in journal (Refereed)
    Abstract [en]

    Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a robust odometry pipeline within a pose graph framework. By evaluation on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it generalizes across environments without needing to change any parameters. We provide the open-source implementation at https://github.com/dan11003/tbv_slam_public

    The full text will be freely available from 2025-06-01 00:00
  • 2.
    Cecchi, Michele
    et al.
    University of Pisa, Pisa, Italy.
    Paiano, Matteo
    University of Pisa, Pisa, Italy.
    Mannucci, Anna
    Örebro University, School of Science and Technology.
    Palleschi, Alessandro
    University of Pisa, Pisa, Italy.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Pallottino, Lucia
    University of Pisa, Pisa, Italy.
    Priority-Based Distributed Coordination for Heterogeneous Multi-Robot Systems with Realistic Assumptions2021In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 6, no 3, p. 6131-6138Article in journal (Refereed)
    Abstract [en]

    A standing challenge in current intralogistics is to reliably, effectively yet safely coordinate large-scale, heterogeneous multi-robot fleets without posing constraints on the infrastructure or unrealistic assumptions on robots. A centralized approach, proposed by some of the authors in prior work, allows to overcome these limitations with medium-scale fleets (i.e., tens of robots). With the aim of scaling to hundreds of robots, in this paper we explore a de-centralized variant of the same approach. The proposed framework maintains the key features of the original approach, namely, ensuring safety despite uncertainties on robot motions, and generality with respect to robot platforms, motion planners and controllers. We include considerations on liveness and solutions to prevent or recover from deadlocks in specific situations are reported and discussed. We validate the approach empirically with simulated, large, heterogeneous multi-robot fleets (up to 100 robots tested) operating both in benchmark and realistic environments.

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    Priority-Based Distributed Coordination for Heterogeneous Multi-Robot Systems with Realistic Assumptions
  • 3.
    Della Corte, Bartolomeo
    et al.
    Department of Computer, Control, and Management Engineering “Antonio Ruberti” Sapienza, University of Rome, Rome, Italy.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Grisetti, Giorgio
    Department of Computer, Control, and Management Engineering “Antonio Ruberti” Sapienza, University of Rome, Rome, Italy.
    Unified Motion-Based Calibration of Mobile Multi-Sensor Platforms With Time Delay Estimation2019In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 4, no 2, p. 902-909Article in journal (Refereed)
    Abstract [en]

    The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it, and their time delays. In this letter, we present a unified pipeline for motion-based calibration of mobile platforms equipped with multiple heterogeneous sensors. We formulate a unified optimization problem to concurrently estimate the platform kinematic parameters, the sensors extrinsic parameters, and their time delays. We analyze the influence of the trajectory followed by the robot on the accuracy of the estimate. Our framework automatically selects appropriate trajectories to maximize the information gathered and to obtain a more accurate parameters estimate. In combination with that, our pipeline observes the parameters evolution in long-term operation to detect possible values change in the parameters set. The experiments conducted on real data show a smooth convergence along with the ability to detect changes in parameters value. We release an open-source version of our framework to the community.

  • 4.
    Dominguez, David Caceres
    et al.
    Örebro University, School of Science and Technology.
    Iannotta, Marco
    Örebro University, School of Science and Technology.
    Stork, Johannes Andreas
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    A Stack-of-Tasks Approach Combined With Behavior Trees: A New Framework for Robot Control2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 4, p. 12110-12117Article in journal (Refereed)
    Abstract [en]

    Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this approach solves a sequence of Quadratic Programs (QP) at each time-step, without taking into account any temporal state evolution, it is suitable for dealing with local disturbances. However, its limitation lies in the handling of situations that require non-quadratic objectives to achieve a specific goal, as well as situations where countering the control disturbance would require a locally suboptimal action. Recent works address this shortcoming by exploiting Finite State Machines (FSMs) to compose the tasks in such a way that the robot does not get stuck in local minima. Nevertheless, the intrinsic trade-off between reactivity and modularity that characterizes FSMs makes them impractical for defining reactive behaviors in dynamic environments. In this letter, we combine the SoT control strategy with Behavior Trees (BTs), a task switching structure that addresses some of the limitations of the FSMs in terms of reactivity, modularity and re-usability. Experimental results on a Franka Emika Panda 7-DOF manipulator show the robustness of our framework, that allows the robot to benefit from the reactivity of both SoT and BTs.

  • 5.
    Forte, Paolo
    et al.
    Örebro University, School of Science and Technology.
    Mannucci, Anna
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Online Task Assignment and Coordination in Multi-Robot Fleets2021In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 6, no 3, p. 4584-4591Article in journal (Refereed)
    Abstract [en]

    We propose a loosely-coupled framework for integrated task assignment, motion planning, coordination and contro of heterogeneous fleets of robots subject to non-cooperative tasks. The approach accounts for the important real-world requiremen that tasks can be posted asynchronously. We exploit systematic search for optimal task assignment, where interference is considered as a cost and estimated with knowledge of the kinodynamic models and current state of the robots. Safety is guaranteed by an online coordination algorithm, where the absence of collisions is treated as a hard constraint. The relation between the weight of interference cost in task assignment and computational overhead is analyzed empirically, and the approach is compared against alternative realizations using local search algorithms for task assignment.

  • 6.
    Gabellieri, Chiara
    et al.
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Palleschi, Alessandro
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Mannucci, Anna
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Pierallini, Michele
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Stefanini, Elisa
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Catalano, Manuel G.
    Istituto Italiano di Tecnologia, Genova GE, Italy.
    Caporale, Danilo
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Settimi, Alessandro
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Garabini, Manolo
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Pallottino, Lucia
    Centro di Ricerca “E. Piaggio” e Departimento di Ingnegneria dell’Informazione, Università di Pisa, Pisa, Italia.
    Towards an Autonomous Unwrapping System for Intralogistics2019In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 4, no 4, p. 4603-4610Article in journal (Refereed)
    Abstract [en]

    Warehouse logistics is a rapidly growing market for robots. However, one key procedure that has not received much attention is the unwrapping of pallets to prepare them for objects picking. In fact, to prevent the goods from falling and to protect them, pallets are normally wrapped in plastic when they enter the warehouse. Currently, unwrapping is mainly performed by human operators, due to the complexity of its planning and control phases. Autonomous solutions exist, but usually they are designed for specific situations, require a large footprint and are characterized by low flexibility. In this work, we propose a novel integrated robotic solution for autonomous plastic film removal relying on an impedance-controlled robot. The main contribution is twofold: on one side, a strategy to plan Cartesian impedance and trajectory to execute the cut without damaging the goods is discussed; on the other side, we present a cutting device that we designed for this purpose. The proposed solution presents the characteristics of high versatility and the need for a reduced footprint, due to the adopted technologies and the integration with a mobile base. Experimental results are shown to validate the proposed approach.

    Download full text (pdf)
    Towards an Autonomous Unwrapping System for Intralogistics
  • 7.
    Gholami Shahbandi, Saeed
    et al.
    Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Iagnemma, Karl
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge MA, USA.
    Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer2018In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 3, no 3, p. 2040-2047Article in journal (Refereed)
    Abstract [en]

    We propose a method based on a nonlinear transformation for nonrigid alignment of maps of different modalities, exemplified with matching partial and deformed two-dimensional maps to layout maps. For two types of indoor environments, over a dataset of 40 maps, we have compared the method to state-of-the-art map matching and nonrigid image registration methods and demonstrate a success rate of 80.41% and a mean point-to-point alignment error of 1.78 m, compared to 31.9% and 10.7 m for the best alternative method. We also propose a fitness measure that can quite reliably detect bad alignments. Finally, we show a use case of transferring prior knowledge (labels/segmentation), demonstrating that map segmentation is more consistent when transferred from an aligned layout map than when operating directly on partial maps (95.97% vs. 81.56%).

    Download full text (pdf)
    Nonlinear Optimization of Multimodal 2D Map Alignment with Application to Prior Knowledge Transfer
  • 8.
    Gugliermo, Simona
    et al.
    Örebro University, School of Science and Technology. Intelligent Transport Systems, Scania CV AB, Södertälje, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Koniaris, Christos
    Intelligent Transport Systems, Scania CV AB, Södertälje, Sweden.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization2023In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3534-3541Article in journal (Refereed)
    Abstract [en]

    The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.

    The full text will be freely available from 2025-06-01 00:00
  • 9.
    Hang, Kaiyu
    et al.
    Robotics, Perception, and Learning Lab, KTH Royal Institute of Technology, Stockholm, Sweden.
    Stork, Johannes Andreas
    Robotics, Perception, and Learning Lab, KTH Royal Institute of Technology, Stockholm, Sweden.
    Pollard, Nancy S.
    Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
    Kragic, Danica
    Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
    A Framework For Optimal Grasp Contact Planning2017In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 2, no 2, p. 704-711Article in journal (Refereed)
    Abstract [en]

    We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all grasps and in each step along a path grasps are removed. For this, we introduce and formally characterize search space structure and cost functions under which minimal cost paths correspond to optimal grasps. Our formulation avoids expensive exhaustive search and reduces computational cost by several orders of magnitude. We present admissible heuristic functions and exploit approximate heuristic search to further reduce the computational cost while maintaining bounded suboptimality for resulting grasps. We exemplify our formulation with point-contact grasping for which we define domain specific heuristics and demonstrate optimality and bounded suboptimality by comparing against exhaustive and uniform cost search on example objects. Furthermore, we explain how to restrict the search graph to satisfy grasp constraints for modeling hand kinematics. We also analyze our algorithm empirically in terms of created and visited search states and resultant effective branching factor.

  • 10.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Fan, Han
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots2017In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 2, no 2, p. 1117-1123Article in journal (Refereed)
    Abstract [en]

    For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling<?brk?> algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback&ndash;Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.

  • 11.
    Hoang, Dinh-Cuong
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Panoptic 3D Mapping and Object Pose Estimation Using Adaptively Weighted Semantic Information2020In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 2, p. 1962-1969Article in journal (Refereed)
    Abstract [en]

    We present a system capable of reconstructing highly detailed object-level models and estimating the 6D pose of objects by means of an RGB-D camera. In this work, we integrate deep-learning-based semantic segmentation, instance segmentation, and 6D object pose estimation into a state of the art RGB-D mapping system. We leverage the pipeline of ElasticFusion as a backbone and propose modifications of the registration cost function to make full use of the semantic class labels in the process. The proposed objective function features tunable weights for the depth, appearance, and semantic information channels, which are learned from data. A fast semantic segmentation and registration weight prediction convolutional neural network (Fast-RGBD-SSWP) suited to efficient computation is introduced. In addition, our approach explores performing 6D object pose estimation from multiple viewpoints supported by the high-quality reconstruction system. The developed method has been verified through experimental validation on the YCB-Video dataset and a dataset of warehouse objects. Our results confirm that the proposed system performs favorably in terms of surface reconstruction, segmentation quality, and accurate object pose estimation in comparison to other state-of-the-art systems. Our code and video are available at https://sites.google.com/view/panoptic-mope.

  • 12.
    Hoang, Dinh-Cuong
    et al.
    CT Department, FPT University, Hanoi, Vietnam.
    Stork, Johannes A.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology. Department of Computing and Software, McMaster University, Hamilton ON, Canada.
    Voting and Attention-Based Pose Relation Learning for Object Pose Estimation From 3D Point Clouds2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 4, p. 8980-8987Article in journal (Refereed)
    Abstract [en]

    Estimating the 6DOF pose of objects is an important function in many applications, such as robot manipulation or augmented reality. However, accurate and fast pose estimation from 3D point clouds is challenging, because of the complexity of object shapes, measurement noise, and presence of occlusions. We address this challenging task using an end-to-end learning approach for object pose estimation given a raw point cloud input. Our architecture pools geometric features together using a self-attention mechanism and adopts a deep Hough voting scheme for pose proposal generation. To build robustness to occlusion, the proposed network generates candidates by casting votes and accumulating evidence for object locations. Specifically, our model learns higher-level features by leveraging the dependency of object parts and object instances, thereby boosting the performance of object pose estimation. Our experiments show that our method outperforms state-of-the-art approaches in public benchmarks including the Sileane dataset 135 and the Fraunhofer IPA dataset [36]. We also deploy our proposed method to a real robot pick-and-place based on the estimated pose.

  • 13.
    Ivan, Jean-Paul A.
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Stork, Johannes A.
    Örebro University, School of Science and Technology.
    Online Distance Field Priors for Gaussian Process Implicit Surfaces2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 4, p. 8996-9003Article in journal (Refereed)
    Abstract [en]

    Gaussian process (GP) implicit surface models provide environment and object representations which elegantly address noise and uncertainty while remaining sufficiently flexible to capture complex geometry. However, GP models quickly become intractable as the size of the observation set grows-a trait which is difficult to reconcile with the rate at which modern range sensors produce data. Furthermore, naive applications of GPs to implicit surface models allocate model resources uniformly, thus using precious resources to capture simple geometry. In contrast to prior work addressing these challenges though model sparsification, spatial partitioning, or ad-hoc filtering, we propose introducing model bias online through the GP's mean function. We achieve more accurate distance fields using smaller models by creating a distance field prior from features which are easy to extract and have analytic distance fields. In particular, we demonstrate this approach using linear features. We show the proposed distance field halves model size in a 2D mapping task using data from a SICK S300 sensor. When applied to a single 3D scene from the TUM RGB-D SLAM dataset, we achieve a fivefold reduction in model size. Our proposed prior results in more accurate GP implicit surfaces, while allowing existing models to function in larger environments or with larger spatial partitions due to reduced model size.

  • 14.
    Krajník, Tomáš
    et al.
    Faculty of Electrical Engineering, Czech Technical University, Praha, Czechia.
    Vintr, Tomáš
    Faculty of Electrical Engineering, Czech Technical University, Praha, Czechia.
    Molina, Sergi
    Lincoln Centre for Autonomous Systems, University of Lincoln, Lincoln, England.
    Fentanes, Jairne P.
    Lincoln Centre for Autonomous Systems, University of Lincoln, Lincoln, England.
    Cielniak, Grzegorz
    Lincoln Centre for Autonomous Systems, University of Lincoln, Lincoln, England.
    Martinez Mozos, Oscar
    Technical University of Cartagena, Cartagena, Spain.
    Broughton, George
    Faculty of Electrical Engineering, Czech Technical University, Praha, Czechia.
    Duckett, Tom
    Lincoln Centre for Autonomous Systems, University of Lincoln, Lincoln, England.
    Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots2019In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 4, no 4, p. 3310-3317Article in journal (Refereed)
    Abstract [en]

    This letter presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modeling long-term, pseudo-periodic variations caused by human activities or natural processes. Unlike previous approaches, the proposed method does not treat time and space separately, and its continuous nature respects both the temporal and spatial continuity of the modeled phenomena. The key idea is to extend the spatial model with a set of wrapped time dimensions that represent the periodicities of the observed events. By performing clustering over this extended representation, we obtain a model that allows the prediction of probabilistic distributions of future states and events in both discrete and continuous spatial representations. We apply the proposed algorithm to several long-term datasets acquired by mobile robots and show that the method enables a robot to predict future states of representations with different dimensions. The experiments further show that the method achieves more accurate predictions than the previous state of the art.

  • 15.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor Manuel
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed)
    Abstract [en]

    Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.

    Download full text (pdf)
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
  • 16.
    Kurtser, Polina
    et al.
    Örebro University, School of Science and Technology.
    Ringdahl, Ola
    Department of Computing Science, Umeå University, Umeå, Sweden.
    Rotstein, Nati
    Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.
    Berenstein, Ron
    Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Rishon Lezion, Israel.
    Edan, Yael
    Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.
    In-field grape cluster size assessment for vine yield estimation using a mobile robot and a consumer level RGB-D camera2020In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 2, p. 2031-2038Article in journal (Refereed)
    Abstract [en]

    Current practice for vine yield estimation is based on RGB cameras and has limited performance. In this paper we present a method for outdoor vine yield estimation using a consumer grade RGB-D camera mounted on a mobile robotic platform. An algorithm for automatic grape cluster size estimation using depth information is evaluated both in controlled outdoor conditions and in commercial vineyard conditions. Ten video scans (3 camera viewpoints with 2 different backgrounds and 2 natural light conditions), acquired from a controlled outdoor experiment and a commercial vineyard setup, are used for analyses. The collected dataset (GRAPES3D) is released to the public. A total of 4542 regions of 49 grape clusters were manually labeled by a human annotator for comparison. Eight variations of the algorithm are assessed, both for manually labeled and auto-detected regions. The effect of viewpoint, presence of an artificial background, and the human annotator are analyzed using statistical tools. Results show 2.8-3.5 cm average error for all acquired data and reveal the potential of using lowcost commercial RGB-D cameras for improved robotic yield estimation.

  • 17.
    Lagriffoul, Fabien
    et al.
    Örebro University, School of Science and Technology.
    Dantam, Neil T.
    Colorado School of Mines, Golden CO, USA.
    Garrett, Caelan
    Massachusetts Institute of Technology, Cambridge MA, USA.
    Akbari, Aliakbar
    Universidad Politécnica de Catalunya, Barcelona, Spain.
    Srivastava, Siddharth
    Arizona State University, Tempe AZ, USA.
    Kavraki, Lydia E.
    Rice University, Houston TX, USA .
    Platform-Independent Benchmarks for Task and Motion Planning2018In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 3, no 4, p. 3765-3772Article in journal (Refereed)
    Abstract [en]

    We present the first platform-independent evaluation method for task and motion planning (TAMP). Previously point, various problems have been used to test individual planners for specific aspects of TAMP. However, no common set of metrics, formats, and problems have been accepted by the community. We propose a set of benchmark problems covering the challenging aspects of TAMP and a planner-independent specification format for these problems. Our objective is to better evaluate and compare TAMP planners, foster communication, and progress within the field, and lay a foundation to better understand this class of planning problems.

  • 18.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lightweight, Viewpoint-Invariant Visual Place Recognition in Changing Environments2018In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 3, no 2, p. 957-964Article in journal (Refereed)
    Abstract [en]

    This paper presents a viewpoint-invariant place recognition algorithm which is robust to changing environments while requiring only a small memory footprint. It demonstrates that condition-invariant local features can be combined with Vectors of Locally Aggregated Descriptors (VLAD) to reduce high-dimensional representations of images to compact binary signatures while retaining place matching capability across visually dissimilar conditions. This system provides a speed-up of two orders of magnitude over direct feature matching, and outperforms a bag-of-visual-words approach with near-identical computation speed and memory footprint. The experimental results show that single-image place matching from non-aligned images can be achieved in visually changing environments with as few as 256 bits (32 bytes) per image.

  • 19.
    Luperto, Matteo
    et al.
    University of Milan, Milan, Italy.
    Kucner, Tomasz Piotr
    Örebro University, Örebro, Sweden; Aalto University, Espoo, Finland .
    Tassi, Andrea
    Politecnico di Milano, Milan, Italy.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Amigoni, Francesco
    Politecnico di Milano, Milan, Italy.
    Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments From Occupancy Grid Maps2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 3, p. 7974-7981Article in journal (Refereed)
    Abstract [en]

    Identifying the environment's structure, through detecting core components such as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonomous mobile robots, including semantic environment understanding. These robots often rely on 2D occupancy maps for core tasks such as localisation and motion and task planning. However, reliable identification of structure and room segmentation from 2D occupancy maps is still an open problem due to clutter (e.g., furniture and movable objects), occlusions, and partial coverage. We propose a method for the RObust StructurE identification and ROom SEgmentation (ROSE2) of 2D occupancy maps thatmay be cluttered and incomplete. ROSE2 identifies the main directions of walls and is resilient to clutter and partial observations, allowing to extract a clean, abstract geometrical floor-plan-like description of the environment, which is used to segment, i.e., to identify rooms in, the original occupancy grid map. ROSE2 is tested in several real-world publicly available cluttered maps obtained in different conditions. The results show that it can robustly identify the environment structure in 2D occupancy maps suffering fromclutter and partial observations, while significantly improving room segmentation accuracy. Thanks to the combination of clutter removal and robust room segmentation, ROSE2 consistently achieves higher performance than the state-of-the-art methods, against which it is compared.

  • 20.
    Mannucci, Anna
    et al.
    Research Center E. Piaggio, University of Pisa, Pisa, Italy.
    Pallottino, Lucia
    Research Center E. Piaggio, University of Pisa, Pisa, Italy.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Provably Safe Multi-Robot Coordination With Unreliable Communication2019In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 4, no 4, p. 3232-3239Article in journal (Refereed)
    Abstract [en]

    Coordination is a core problem in multi-robot systems, since it is a key to ensure safety and efficiency. Both centralized and decentralized solutions have been proposed, however, most assume perfect communication. This letter proposes a centralized method that removes this assumption, and is suitable for fleets of robots driven by generic second-order dynamics. We formally prove that: first, safety is guaranteed if communication errors are limited to delays; and second, the probability of unsafety is bounded by a function of the channel model in networks with packet loss. The approach exploits knowledge of the network's non-idealities to ensure the best possible performance of the fleet. The method is validated via several experiments with simulated robots.

  • 21.
    Palmieri, Luigi
    et al.
    Robert Bosch GmbH Corp Res, Gerlingen, Germany.
    Rudenko, Andrey
    Örebro University, School of Science and Technology. Robert Bosch GmbH Corp Res, Gerlingen, Germany.
    Mainprice, Jim
    University of Stuttgart, Stuttgart, Germany.
    Hanheide, Marc
    University of Lincoln, Lincoln, England.
    Alahi, Alexandre
    Ecole Polytech Fed Lausanne, Lausanne, Switzerland.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Arras, Kai O.
    Robert Bosch GmbH Corp Res, Robot Program, Gerlingen, Germany.
    Guest Editorial: Introduction to the Special Issue on Long-Term Human Motion Prediction2021In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 6, no 3, p. 5613-5617Article in journal (Other academic)
    Abstract [en]

    The articles in this special section focus on long term human motion prediction. This represents a key ability for advanced autonomous systems, especially if they operate in densely crowded and highly dynamic environments. In those settings understanding and anticipating human movements is fundamental for robust long-term operation of robotic systems and safe human-robot collaboration. Foreseeing how a scene with multiple agents evolves over time and incorporating predictions in a proactive manner allows for novel ways of planning and control, active perception, or humanrobot interaction. Recent planning and control approaches use predictive techniques to better cope with the dynamics of the environment, thus allowing the generation of smoother and more legible robot motion. Predictions can be provided as input to the planning or optimization algorithm (e.g. as a cost term or heuristic function), or as additional dimension to consider in the problem formulation (leading to an increased computational complexity). Recent perception techniques deeply interconnect prediction modules with detection, segmentation and tracking, to generally increase the accuracy of different inference tasks, i.e. filtering, predicting. As also indicated by some of the scientific works accepted in this special issue, novel deep learning architectures allow better interleaving of the aforementioned units.

  • 22.
    Rudenko, Andrey
    et al.
    Örebro University, School of Science and Technology. Robotics Research, Bosch Corporate Research, Stuttgart, Germany.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Swaminathan, Chittaranjan Srinivas
    Örebro University, School of Science and Technology.
    Chadalavada, Ravi Teja
    Örebro University, School of Science and Technology.
    Arras, Kai O.
    Robotics Research, Bosch Corporate Research, Stuttgart, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    THÖR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset2020In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 2, p. 676-682Article in journal (Refereed)
    Abstract [en]

    Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present THÖR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. THÖR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies.

  • 23.
    Rudenko, Andrey
    et al.
    Örebro University, School of Science and Technology. Bosch Corporate Research, Renningen, Germany.
    Palmieri, Luigi
    Bosch Corporate Research, Renningen, Germany.
    Doellinger, Johannes
    Bosch Center for Artificial Intelligence, Renningen, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Arras, Kai O.
    Bosch Corporate Research, Renningen, Germany.
    Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments2021In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 6, no 2, p. 3248-3255Article in journal (Refereed)
    Abstract [en]

    Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from demonstrations with on-site collected trajectory data is a powerful approach to discover recurrent motion patterns, generalization to new environments, where sufficient motion data are not readily available, remains a challenge. In many cases, however, semantic information about the environment is a highly informative cue for the prediction of pedestrian motion or the estimation of collision risks. In this work, we infer occupancy priors of human motion using only semantic environment information as input. To this end, we apply and discuss a traditional Inverse Optimal Control approach, and propose a novel approach based on Convolutional Neural Networks (CNN) to predict future occupancy maps. Our CNN method produces flexible context-aware occupancy estimations for semantically uniform map regions and generalizes well already with small amounts of training data. Evaluated on synthetic and real-world data, it shows superior results compared to several baselines, marking a qualitative step-up in semantic environment assessment.

  • 24.
    Yang, Quantao
    et al.
    Örebro University, School of Science and Technology.
    Dürr, Alexander
    Department of Computer Science, Faculty of Engineering (LTH), Lund University, Lund, Sweden.
    Topp, Elin Anna
    Department of Computer Science, Faculty of Engineering (LTH), Lund University, Lund, Sweden.
    Stork, Johannes A.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology. Department of Computing and Software, McMaster University, Hamilton ON, Canada .
    Variable Impedance Skill Learning for Contact-Rich Manipulation2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 3, p. 8391-8398Article in journal (Refereed)
    Abstract [en]

    Contact-rich manipulation tasks remain a hard problem in robotics that requires interaction with unstructured environments. Reinforcement Learning (RL) is one potential solution to such problems, as it has been successfully demonstrated on complex continuous control tasks. Nevertheless, current state-of-the-art methods require policy training in simulation to prevent undesired behavior and later domain transfer even for simple skills involving contact. In this paper, we address the problem of learning contact-rich manipulation policies by extending an existing skill-based RL framework with a variable impedance action space. Our method leverages a small set of suboptimal demonstration trajectories and learns from both position, but also crucially impedance-space information. We evaluate our method on a number of peg-in-hole task variants with a Franka Panda arm and demonstrate that learning variable impedance actions for RL in Cartesian space can be deployed directly on the real robot, without resorting to learning in simulation.

    Download full text (pdf)
    Variable Impedance Skill Learning for Contact-Rich Manipulation
  • 25.
    Yang, Quantao
    et al.
    Örebro University, School of Science and Technology.
    Stork, Johannes A.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology. Department of Computing and Software, McMaster University, Canada.
    MPR-RL: Multi-Prior Regularized Reinforcement Learning for Knowledge Transfer2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 3, p. 7652-7659Article in journal (Refereed)
    Abstract [en]

    In manufacturing, assembly tasks have been a challenge for learning algorithms due to variant dynamics of different environments. Reinforcement learning (RL) is a promising framework to automatically learn these tasks, yet it is still not easy to apply a learned policy or skill, that is the ability of solving a task, to a similar environment even if the deployment conditions are only slightly different. In this letter, we address the challenge of transferring knowledge within a family of similar tasks by leveraging multiple skill priors. We propose to learn prior distribution over the specific skill required to accomplish each task and compose the family of skill priors to guide learning the policy for a new task by comparing the similarity between the target task and the prior ones. Our method learns a latent action space representing the skill embedding from demonstrated trajectories for each prior task. We have evaluated our method on a task in simulation and a set of peg-in-hole insertion tasks and demonstrate better generalization to new tasks that have never been encountered during training. Our Multi-Prior Regularized RL (MPR-RL) method is deployed directly on a real world Franka Panda arm, requiring only a set of demonstrated trajectories from similar, but crucially not identical, problem instances.

  • 26.
    Yang, Yuxuan
    et al.
    Örebro University, School of Science and Technology.
    Stork, Johannes A.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Particle Filters in Latent Space for Robust Deformable Linear Object Tracking2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 4, p. 12577-12584Article in journal (Refereed)
    Abstract [en]

    Tracking of deformable linear objects (DLOs) is important for many robotic applications. However, achieving robust and accurate tracking is challenging due to the lack of distinctive features or appearance on the DLO, the object's high-dimensional state space, and the presence of occlusion. In this letter, we propose a method for tracking the state of a DLO by applying a particle filter approach within a lower-dimensional state embedding learned by an autoencoder. The dimensionality reduction preserves state variation, while simultaneously enabling a particle filter to accurately track DLO state evolution with a practically feasible number of particles. Compared to previous works, our method requires neither running a high-fidelity physics simulation, nor manual designs of constraints and regularization. Without the assumption of knowing the initial DLO state, our method can achieve accurate tracking even under complex DLO motions and in the presence of severe occlusions.

  • 27.
    Zhang, Shiyu
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Online Sequential Task Assignment With Execution Uncertainties for Multiple Robot Manipulators2021In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 6, no 4, p. 6993-7000Article in journal (Refereed)
    Abstract [en]

    In order to let multiple robot manipulators cooperatively complete a sequence of tasks in a shared workspace under task execution uncertainty, this letter proposes a multi-robot task allocation framework for constantly assigning tasks to robots, while the interference among concurrent robot motions is account for. An online sequential task assignment method is presented, which decouples the time-extended problem into a sequence of synchronous and asynchronous instantaneous assignment sub-problems. This renders the approach capable of reacting to task execution uncertainties in real-time. A one-step-ahead simulation method is employed to reduce the idle time of robots and improve task completion efficiency. Each instantaneous assignment sub-problem is modeled as an optimal assignment problem with variable utility and solved by a branch-and-bound algorithm, with which multi-robot motion coordination is integrated. Experimental results conducted with three Franka-Emika Panda arms show that these can cooperatively complete all tasks without collision and little waiting time. Simulations with larger multi-robot systems show that the approach scales linearly with the number of robots.

1 - 27 of 27
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