43 research outputs found

    Spatiotemporal models for motion planning in human populated environments

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    In this paper we present an effective spatio-temporal model for motion planning computed using a novel representation known as the temporary warp space-hypertime continuum. Such a model is suitable for robots that are expected to be helpful to humans in their natural environments. This method allows to capture natural periodicities of human behavior by adding additional time dimensions. The model created thus represents the temporal structure of the human habits within a given space and can be analyzed using regular analytical methods. We visualize the results on a real-world dataset using heatmaps

    Learning State-Space Models for Mapping Spatial Motion Patterns

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    Mapping the surrounding environment is essential for the successful operation of autonomous robots. While extensive research has focused on mapping geometric structures and static objects, the environment is also influenced by the movement of dynamic objects. Incorporating information about spatial motion patterns can allow mobile robots to navigate and operate successfully in populated areas. In this paper, we propose a deep state-space model that learns the map representations of spatial motion patterns and how they change over time at a certain place. To evaluate our methods, we use two different datasets: one generated dataset with specific motion patterns and another with real-world pedestrian data. We test the performance of our model by evaluating its learning ability, mapping quality, and application to downstream tasks. The results demonstrate that our model can effectively learn the corresponding motion pattern, and has the potential to be applied to robotic application tasks.Comment: 6 pages, 5 figures, to be published in ECMR 2023 conference proceeding

    Survey of maps of dynamics for mobile robots

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    Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area

    HMM-Based Dynamic Mapping with Gaussian Random Fields

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    This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments

    Warped Hypertime Representations for Long-Term Autonomy of Mobile Robots

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    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

    Context-sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry

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    Computational methods for peptide identification via tandem mass spectrometry (MS/MS) lie at the heart of proteomic characterization of biological samples. Due to the complex nature of peptide fragmentation process inside mass spectrometers, most extant methods underutilize the intensity information available in the tandem mass spectrum. Further, high noise content and variability in MS/MS datasets present significant data analysis challenges. These factors contribute to loss of identifications, necessitating development of more complex approaches. This dissertation develops and evaluates a novel probabilistic framework called Context-Sensitive Peptide Identification (CSPI) for improving peptide scoring and identification from MS/MS data. Employing Input-Output Hidden Markov Models (IO-HMM), CSPI addresses the above computational challenges by modeling the effect of peptide physicochemical features ("context") on their observed (normalized) MS/MS spectrum intensities. Flexibility and scalability of the CSPI framework enables incorporation of many different kinds of features from the domain into the modeling task. Design choices also include the underlying parameter representation and allow learning complex probability distributions and dependencies embedded in the data. Empirical evaluation on multiple datasets of varying sizes and complexity demonstrates that CSPI's intensity-based scores significantly improve peptide identification performance, identifying up to ~25% more peptides at 1% False Discovery Rate (FDR) as compared with popular state-of-the-art approaches. It is further shown that a weighted score combination procedure that includes CSPI scores along with other commonly used scores leads to greater discrimination between true and false identifications, achieving ~4-8% more correct identifications at 1% FDR compared with the case without CSPI features. Superior performance of the CSPI framework has the potential to impact downstream proteomic investigations (like protein identification, quantification and differential expression) that utilize results from peptide-level analyses. Being computationally intensive, the design and implementation of CSPI supports efficient handling of large MS/MS datasets, achieved through database indexing and parallelization of the computational workflow using multiprocessing architecture
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