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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Kevin Daun Oskar Von Stryk Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Darmstadt Germany

While rescue robots are becoming more established as part of disaster response, they are typically teleoperated in actual disasters. (Autonomous) assistance functions can improve performance, extend functionality and reduce operator overload. It is necessary to understand relevant requirements to ensure that developed capabilities apply to real-world needs. Previous analyses focused on general aspects of rescue robots, leaving a gap in understanding requirements for (autonomous) assistance functions. We address this gap and provide a detailed, evidence-driven analysis of application requirements and research challenges for (autonomous) assistance functions for rescue robots in reconnaissance missions. We base our analysis on a comprehensive model for technology acceptance and consider reports of past deployments, related analyses, our own experience from deploying robots, and insights from workshops with first responders. We define relevant aspects of an integrated function capability and analyze general and specific requirements for assistance functions and autonomy. We relate our results with current assistance functions and identify several research challenges. A key insight is the need for an increased research focus on novel approaches combining the complementary capabilities of human operators and robotic assistance functions.

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Efficient Dynamic LiDAR Odometry for Mobile Robots with Structured Point Clouds

arXiv

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arXiv 2024年

作者： Lichtenfeld, Jonathan Daun, Kevin Stryk, Oskar von Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Hochschulstr. 10 Darmstadt64289 Germany

We propose a real-time dynamic LiDAR odometry pipeline for mobile robots in Urban Search and Rescue (USAR) scenarios. Existing approaches to dynamic object detection often rely on pretrained learned networks or computationally expensive volumetric maps. To enhance efficiency on computationally limited robots, we reuse data between the odometry and detection module. Utilizing a range image segmentation technique and a novel residual-based heuristic, our method distinguishes dynamic from static objects before integrating them into the point cloud map. The approach demonstrates robust object tracking and improved map accuracy in environments with numerous dynamic objects. Even highly non-rigid objects, such as running humans, are accurately detected at point level without prior downsampling of the point cloud and hence, without loss of information. Evaluation on simulated and real-world data validates its computational efficiency. Compared to a state-of-the-art volumetric method, our approach shows comparable detection performance at a fraction of the processing time, adding only 14 ms to the odometry module for dynamic object detection and tracking. The implementation and a new real-world dataset are available as open-source for further research. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Hector UI: A Flexible Human-Robot User Interface for (Semi-)Autonomous Rescue and Inspection Robots

Hector UI: A Flexible Human-Robot User Interface for (Semi-)...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Stefan Fabian Oskar von Stryk Computer Science Department Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Germany

The remote human operator's user interface (UI) is an important link to make the robot an efficient extension of the operator's perception and action. In rescue applications, several studies have investigated the design of operator interfaces based on observations during major robotics competitions or field deployments. Based on this research, guidelines for good interface design were empirically identified. The investigations on the UIs of teams participating in competitions are often based on external observations during UI application, which may miss some relevant requirements for UI flexibility. In this work, we present an open-source and flexibly configurable user interface based on established guidelines and its exemplary use for wheeled, tracked, and walking robots. We explain the design decisions and cover the insights we have gained during its highly successful applications in multiple robotics competitions and evaluations. The presented UI can also be adapted for other robots with little effort and is available as open source.

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3D Coverage Path Planning for Efficient Construction Progress Monitoring

3D Coverage Path Planning for Efficient Construction Progres...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Katrin Becker Martin Oehler Oskar Von Stryk Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Germany

ISBN: (纸本)9781665456814

Ahstract- On construction sites, progress must be monitored continuously to ensure that the current state corresponds to the planned state in order to increase efficiency, safety and detect construction defects at an early stage. Autonomous mobile robotscan document the state of construction with high data quality and consistency. However, finding a path that fully covers the construction site is a challenging task as it can be large, slowly changing over time, and contain dynamic objects. Existing approaches are either exploration approaches that require a long time to explore the entire building, object scanning approaches that are not suitable for large and complex buildings, or planning approaches that only consider 2D coverage. In this paper, we present a novel approach for planning an efficient 3D path for progress monitoring on large construction sites with multiple levels. By making use of an existing 3D model we ensure that all surfaces of the building are covered by the sensor payload such as a 360-degree camera or a lidar. This enables the consistent and reliable monitoring of construction site progress with an autonomous ground robot. We demonstrate the effectiveness of the proposed planner on an artificial and a real building model, showing that much shorter paths and better coverage are achieved than with a traditional exploration planner.

关键词： Solid modeling Three-dimensional displays Buildings Path planning Planning Safety Security

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Multi-Cam ARM-SLAM: Robust Multi-Modal State Estimation Using Truncated Signed Distance Functions for Mobile Rescue Robots

Multi-Cam ARM-SLAM: Robust Multi-Modal State Estimation Usin...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Jasper Süβ Marius Schnaubelt Oskar Von Stryk Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Darmstadt Germany

ISBN: (纸本)9781665456814

To be able to perform manipulation tasks within an unknown environment, rescue robots require a detailed model of their surroundings, which is often generated using registered depth images as an input. However, erroneous camera registrations due to noisy motor encoder readings, a faulty kinematic model or other error sources can drastically reduce the model quality. Most existing approaches register the pose of a free-floating single camera without considering constraints by the kinematic robot configuration. In contrast, ARM-SLAM [1] performs dense localization and mapping in the configuration space of the robot arm, implicitly tracking the pose of a single camera and creating a volumetric model. However, using a single camera only allows to cover a small field of view and can only constrain up to six degrees of freedom. Therefore, we propose the Multi-Cam ARM-SLAM (MC-ARM-SLAM) framework, which fuses information of multiple depth cameras mounted on the robot into a joint model. The use of multiple cameras allows to also estimate the motion of the robot base that is modeled as a virtual kinematic chain additionally to the motion of the arm. Furthermore, we use a robust bivariate error formulation, which helps to boost the accuracy of the method and mitigates the influence of outliers. The proposed method is extensively evaluated in simulation and on a real rescue robot. It is shown that the method is able to correct errors in the motor encoders and the kinematic model and outperforms the base version of ARM-SLAM.

关键词： Robot vision systems Kinematics Cameras Manipulators Safety Registers Security

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Start State Selection for Control Policy Learning from Optimal Trajectories

Start State Selection for Control Policy Learning from Optim...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christoph Zelch Jan Peters Oskar von Stryk Department of Computer Science TU Darmstadt Simulation Systems Optimization and Robotics Group Darmstadt Germany Department of Computer Science Intelligent Autonomous Systems Group TU Darmstadt Darmstadt Germany

Combination of optimal control methods and machine learning approaches allows to profit from complementary benefits of each field in control of robotic systems. Data from optimal trajectories provides valuable information that can be used to learn a near-optimal state-dependent feedback control policy. To obtain high-quality learning data, careful selection of optimal trajectories, determined by a set of start states, is essential to achieve a good learning performance. In this paper, we extend previous work with new comple-menting strategies to generate start points. These methods complement the existing approach, as they introduce new criteria to identify relevant regions in joint state space that need coverage by new trajectories. It is demonstrated that the extensions significantly improve the overall performance of the previous method in simulation on full nonlinear dynamics model of the industrial Manutec r3 robot arm. Further, it is demonstrated that it suffices to learn a policy that reaches the proximity of the goal state, from where a PI controller can be used for stable control reaching the final system state.

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Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features

arXiv

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arXiv 2024年

作者： Zelch, Christoph Peters, Jan von Stryk, Oskar Simulation Systems Optimization and Robotics Group Department of Computer Science TU Darmstadt Hochschulstr. 10 Darmstadt64289 Germany Intelligent Autonomous Systems Group Department of Computer Science TU Darmstadt Hochschulstr. 10 Darmstadt64289 Germany

Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it allows automated analysis of recorded motion data. Many clustering algorithms for trajectories build upon distance metrics that are based on pointwise Euclidean distances. However, our work indicates that focusing on salient characteristics is often sufficient. We present a novel distance measure for motion plans consisting of state and control trajectories that is based on a compressed representation built from their main features. This approach allows a flexible choice of feature classes relevant to the respective task. The distance measure is used in agglomerative hierarchical clustering. We compare our method with the widely used dynamic time warping algorithm on test sets of motion plans for the Furuta pendulum and the Manutec robot arm and on real-world data from a human motion dataset. The proposed method demonstrates slight advantages in clustering and strong advantages in runtime, especially for long trajectories. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Reprojection Methods for Koopman-Based Modelling and Prediction

Reprojection Methods for Koopman-Based Modelling and Predict...

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IEEE Conference on Decision and Control

作者： Pieter Van Goor Robert Mahony Manuel Schaller Karl Worthmann Systems Theory and Robotics Group School of Engineering Australian National University Canberra Australia Optimization-based Control Group Institute of Mathematics Technische Universitat Ilmenau Ilmenau Germany

Extended Dynamic Mode Decomposition (eDMD) is a powerful tool to generate data-driven surrogate models for the prediction and control of nonlinear dynamical systems in the Koopman framework. In eDMD a compression of the lifted system dynamics on the space spanned by finitely many observables is computed, in which the original space is embedded as a low-dimensional manifold. While this manifold is invariant for the infinite-dimensional Koopman operator, this invariance is typically not preserved for its eDMD-based approximation. Hence, an additional (re-)projection step is often tacitly incorporated to improve the prediction capability. We propose a novel framework for consistent reprojectors respecting the underlying manifold structure. Further, we present a new geometric reprojector based on maximum-likelihood arguments, which significantly enhances the approximation accuracy and preserves known finite-data error bounds.

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Reprojection methods for Koopman-based modelling and prediction

arXiv

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arXiv 2023年

作者： van Goor, Pieter Mahony, Robert Schaller, Manuel Worthmann, Karl Systems Theory and Robotics Group School of Engineering Australian National University Canberra Australia Optimization-based Control Group Institute of Mathematics Technische Universität Ilmenau Germany

关键词： Dynamic mode decomposition

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Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features

Clustering of Motion Trajectories by a Distance Measure Base...

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IEEE-RAS International Conference on Humanoid Robots

作者： Christoph Zelch Jan Peters Oskar von Stryk Department of Computer Science TU Darmstadt Hochschulstr. 10 Simulation Systems Optimization and Robotics Group Darmstadt Germany Department of Computer Science TU Darmstadt Hochschulstr. 10 Intelligent Autonomous Systems Group Darmstadt Germany

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