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IEEE International Conference on robotics and Automation (ICRA)

作者： Jon Škerlj Mazin Hamad Jean Elsner Abdeldjallil Naceri Sami Haddadin Department of Computer Engineering School of Computation Information and Technology Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich (TUM)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Integrating humanoid service mobile robots into human environments presents numerous challenges, primarily concerning the safety of interactions between robots and humans. To address these safety concerns, we propose a novel approach that leverages the capabilities of digital twin technology by tailoring it to incorporate comprehensive and robust safety concepts. This paper introduces a "safe-by-design" digital twin that operates alongside the real twin robot in the loop, engaging real-time safety framework during physical interactions with the surrounding environment, including *** validate the effectiveness of our proposed safe-by-design digital twin framework, we conducted experiments using a humanoid service mobile robot alongside simulated human counterparts. Our results demonstrate the capability of the integrated impact safety module within the proposed digital twin approach to limit the velocities of both the robot’s base and arms, adhering to injury biomechanics-based safety thresholds. These findings emphasize the promise of our proposed approach for ensuring the physical safety of humanoid service mobile robots operating in dynamic human environments. It enables the digital twin to preemptively identify potential safety hazards and formulate safe intervention actions to ensure the robot’s compliance with safety regulations, paving the way for safer and more widespread adoption of robotic systems in various service domains.

关键词： Service robots Tracking Humanoid robots Human-robot interaction Robot sensing systems Safety Digital twins

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Autonomous Residual Threshold Detection for Decentralized Radar Inertial Odometry in Hostile Environments

Autonomous Residual Threshold Detection for Decentralized Ra...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Moumita Mukherjee Avijit Banerjee Anton Koval George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

The article introduces an adaptive threshold detection mechanism aimed at enhancing a decentralized radar inertial odometry (RIO) framework, enabling resilient localization in challenging hostile environments. Considering that frequency-modulated continuous-wave (FMCW) radars possess characteristics enabling measurements in challenging indoor and outdoor environments, the fusion of multiple radars in an ensemble configuration, along with an inertial measurement unit (IMU), holds promise in surpassing individual sensor limitations. This approach thereby enhances robust perception. The proposed adaptive autonomous residual threshold mechanism employs a real-time residual analysis to dynamically adjust the sensor fusion process by comparing the variance between two extended Kalman filters. This adaptive approach addresses irregularities in data samples from multiple sensors, thereby enhancing the decentralized smoothing estimator’s precision in providing localization while navigating through hostile environments marked by limited visibility, extreme weather, or high interference. Consequently, it contributes to the resilience and adaptability of autonomous systems in real-world scenarios. The proposed framework effectively showcases precise localization through decentralized radar inertial odometry(RIO).

关键词： Location awareness Smoothing methods Radar measurements Radar detection Radar Sensor fusion Robot sensing systems

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Driving Scene Understanding with Traffic Scene-Assisted Topology Graph Transformer 24

Driving Scene Understanding with Traffic Scene-Assisted Topo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Rong, Fu Peng, Wenjin Lan, Meng Zhang, Qian Zhang, Lefei National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Hong Kong University of Science and Technology Hong Kong Hong Kong Horizon Robotics Beijing China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798400706868

Driving scene topology reasoning aims to understand the objects present in the current road scene and model their topology relationships to provide guidance information for downstream tasks. Previous approaches fail to adequately facilitate interactions among traffic objects and neglect to incorporate scene information into topology reasoning, thus limiting the comprehensive exploration of potential correlations among objects and diminishing the practical significance of the reasoning results. Besides, the lack of constraints on lane direction may introduce erroneous guidance information and lead to a decrease in topology prediction accuracy. In this paper, we propose a novel topology reasoning framework, dubbed TSTGT, to address these issues. Specifically, we design a divide-and-conquer topology graph Transformer to respectively infer the lane-lane and lane-traffic topology relationships, which can effectively aggregate the local and global object information in the driving scene and facilitate the topology relationship learning. Additionally, a traffic scene-assisted reasoning module is devised and combined with the topology graph Transformer to enhance the practical significance of lane-traffic topology. In terms of lane detection, we develop a point-wise matching strategy to infer lane centerlines with correct directions, thereby improving the topology reasoning accuracy. Extensive experimental results on Openlane-V2 benchmark validate the superiority of our TSTGT over state-of-the-art methods and the effectiveness of our proposed modules. The code is available at https://***/rongfu-dsb/TSTGT. © 2024 ACM.

关键词： Graph theory

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On Designing Consistent Covariance Recovery from a Deep Learning Visual Odometry Engine

arXiv

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arXiv 2024年

作者： Nir, Jagatpreet Singh Giaya, Dennis Singh, Hanumant NU Field Robotics lab Department of Electrical and Computer Engineering Northeastern University Boston United States

Deep learning techniques have significantly advanced in providing accurate visual odometry solutions by leveraging large datasets. However, generating uncertainty estimates for these methods remains a challenge. Traditional sensor fusion approaches in a Bayesian framework are well-established, but deep learning techniques with millions of parameters lack efficient methods for uncertainty estimation. This paper addresses the issue of uncertainty estimation for pre-trained deep-learning models in monocular visual odometry. We propose formulating a factor graph on an implicit layer of the deep learning network to recover relative covariance estimates, which allows us to determine the covariance of the visual odometry (VO) solution. We showcase the consistency of the deep learning engine’s covariance approximation with an empirical analysis of the covariance model on the EUROC datasets to demonstrate the correctness of our formulation. © 2024, CC BY.

关键词： Large datasets

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Behavior Replication of Cascaded Dynamic Systems Using Machine Learning

Behavior Replication of Cascaded Dynamic Systems Using Machi...

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2023 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2023

作者： Abouheaf, Mohammed Gueaieb, Wail Mayyas, Mohammad Bowling Green State University Robotics Engineering Bowling GreenOH43403 United States Ottawa University School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada

ISBN: (纸本)9798350308044

This paper introduces an innovative data-driven approach for replicating behaviors in interconnected and heterogeneous dynamic systems. The core concept involves real-time control of dynamic systems to closely mimic reference-model trajectories using model-free techniques. Within this coupled framework, one component possesses complete information about reference-trajectories, although not necessarily their dynamics. In contrast, follower systems, with limited connectivity to reference-model trajectories, exclusively replicate the behavior of the primary process, which retains insight into model-reference dynamics. The adopted strategies are causal, integrating higher-order error dynamics to ensure precise tracking of reference-trajectories. Furthermore, these strategies incorporate variations in reference-model dynamics via a pseudo partial derivative, akin to sensitivity derivatives in model-reference adaptive strategies. To optimize the dynamic behavior of the follower process, the solution employs a reinforcement learning mechanism through adaptive critics. This mechanism approximates the optimal strategy and the associated value function. The actor and critic weights of the adaptive critic structure are tuned using a projection technique to ensure convergence of the adapted strategy. The validation of this solution is demonstrated on a dynamic system with delays, simulating an underwater vehicle scenario. The developed methodology is rigorously compared with another high-order model-free adaptive control approach. The presented approach showcases its capability to effectively replicate behaviors, resulting in improved tracking accuracy. © 2023 IEEE.

关键词： Trajectories

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D-MARL: A Dynamic Communication-Based Action Space Enhancement for Multi Agent Reinforcement Learning Exploration of Large Scale Unknown Environments

D-MARL: A Dynamic Communication-Based Action Space Enhanceme...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Gabriele Calzolari Vidya Sumathy Christoforos Kanellakis George Nikolakopoulos Department of Computer Science Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Sweden

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this article, we propose a novel communication-based action space enhancement for the D-MARL exploration algorithm to improve the efficiency of mapping an unknown environment, represented by an occupancy grid map. In general, communication between autonomous systems is crucial when exploring large and unstructured environments. In such real-world scenarios, data transmission is limited and relies heavily on inter-agent proximity and the attributes of the autonomous platforms. In the proposed approach, each agent’s policy is optimized by utilizing the heterogeneous-agent proximal policy optimization algorithm to autonomously choose whether to communicate or explore the environment. To accomplish this, multiple novel reward functions are formulated by integrating inter-agent communication and exploration. The investigated approach aims to increase efficiency and robustness in the mapping process, minimize exploration overlap, and prevent agent collisions. The D-MARL policies trained on different reward functions have been compared to understand the effect of different reward terms on the collaborative attitude of the homogeneous agents. Finally, multiple simulation results are provided to prove the efficacy of the proposed scheme.

关键词： Autonomous systems Simulation Merging Employment Collaboration Reinforcement learning Robustness Data communication Optimization Intelligent robots

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Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search

arXiv

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arXiv 2024年

作者： Calzolari, Gabriele Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George the Robotics and AI Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from A∗ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Transformers for Image-Goal Navigation

arXiv

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arXiv 2024年

作者： Pelluri, Nikhilanj V. Minnesota Robotics Institute Department of Computer Science and Engineering University of Minnesota Twin Cities United States

Visual perception and navigation have emerged as major focus areas in the field of embodied artificial intelligence. We consider the task of image-goal navigation, where an agent is tasked to navigate to a goal specified by an image, relying only on images from an onboard camera. This task is particularly challenging since it demands robust scene understanding, goal-oriented planning and long-horizon navigation. Most existing approaches typically learn navigation policies reliant on recurrent neural networks trained via online reinforcement learning. However, training such policies requires substantial computational resources and time, and performance of these models is not reliable on long-horizon navigation. In this work, we present a generative Transformer based model that jointly models image goals, camera observations and the robot’s past actions to predict future actions. We use state-of-the-art perception models and navigation policies to learn robust goal conditioned policies without the need for real-time interaction with the environment. Our model demonstrates capability in capturing and associating visual information across long time horizons, helping in effective navigation. © 2024, CC BY.

关键词： Navigation

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TFMarker: A Tangible Fiducial Pattern for Enabling Camera-assisted Guided Landing in SubT Environments

TFMarker: A Tangible Fiducial Pattern for Enabling Camera-as...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Mario A. V. Saucedo Akash Patel Niklas Dahlquist Yifan Bai Björn Lindqvist Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Visual servoing plays a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to aerial manipulation. This article proposes TFMarker, a novel tangible fiducial pattern for enabling camera-assisted guided landing of UAVs by using the visual features from color markers as the main source of information. TFMarker is structured around a 4-point fiducial marker, allowing for accurate, precise, and consistent pose estimation in different environments and lighting conditions, while also offering resilience to motion blur. The presented detection framework is based on a three-step architecture, where the first step uses Gaussian and color filtering in addition to morphological operation in order to generate a robust detection of the markers. The second step uses the Gift Wrapping Algorithm, to organize the same-color markers based on their relative positioning with respect to the off-color marker. Finally, the Perspective-n-Point optimization problem is solved in order to extract the pose (i.e. position and orientation) of the proposed pattern with respect to the vision sensor. The efficacy of the proposed scheme has been extensively validated in indoor and SubT environments for the task of autonomous landing using a custom-made UAV. The experimental results showcase the performance of the proposed method, which presents a better detection rate in both environments while retaining similar accuracy and precision to the baseline approach. For the video of the experimental evaluation please refer to the following link: https://***/Zh13OObp15Q

关键词： Visualization Accuracy Filtering Color Vision sensors Autonomous aerial vehicles Visual servoing Wrapping Optimization Morphological operations

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STAGE: Scalable and Traversability-Aware Graph based Exploration Planner for Dynamically Varying Environments

STAGE: Scalable and Traversability-Aware Graph based Explora...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Akash Patel Mario A. V. Saucedo Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representation, that consists of, i) the local sub-graph and ii) the global graph layer respectively. The local sub-graphs encode local volumetric gain locations as frontiers, based on the direct pointcloud visibility, allowing fast graph building and path planning. Additionally, the global graph is build in an efficient way, using node-edge information exchange only on overlapping regions of sequential sub-graphs. Different from the state-of-the-art graph based exploration methods, the proposed approach efficiently re-uses sub-graphs built in previous iterations to construct the global navigation layer. Another merit of the proposed scheme is the ability to handle scene changes (e.g. blocked pathways), adaptively updating the obstructed part of the global graph from traversable to not-traversable. This operation involved oriented sample space of a path segment in the global graph layer, while removing the respective edges from connected nodes of the global graph in cases of obstructions. As such, the exploration behavior is directing the robot to follow another route in the global re-positioning phase through path-way updates in the global graph. Finally, we showcase the performance of the method both in simulation runs as well as deployed in real-world scene involving a legged robot carrying camera and lidar sensor.

关键词： Legged locomotion Uncertainty Laser radar Navigation Robot vision systems Memory management Cameras

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