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IEEE International Conference on robotic Computing (IRC)

作者： Sahar Salimpour Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

ISBN: (纸本)9781665472616

Automatic detection of visual anomalies and changes in the environment has been a topic of recurrent attention in the fields of machine learning and computer vision over the past decades. A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. The majority of existing approaches focus on anomaly or fault detection in a specific class of images or environments, while general-purpose visual anomaly detection algorithms are more scarce in the literature. In this paper, we propose a comprehensive deep learning framework for detecting anomalies and changes in a priori unknown environments after a reference dataset is gathered, and without need for retraining the model. We use the SuperPoint and SuperGlue feature extraction and matching methods to detect anomalies based on reference images taken from a similar location and with partial overlapping of the field of view. We also introduce a self-calibrating method for the proposed model in order to address the problem of sensitivity to feature matching thresholds and environmental conditions. To evaluate the proposed framework, we have used a ground robot system for the purpose of reference and query data collection. We show that high accuracy can be obtained using the proposed method. We also show that the calibration process enhances changes and foreign object detection performance.

关键词： Training Visualization Image segmentation Sensitivity Object detection Inspection Robot sensing systems

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Distributed robotic systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness

Distributed Robotic Systems in the Edge-Cloud Continuum with...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Jiaqiang Zhang Farhad Keramat Xianjia Yu Daniel Montero Hernández Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

robotic systems are more connected, networked, and distributed than ever. New architectures that comply with the de facto robotics middleware standard, ROS 2, have recently emerged to fill the gap in terms of hybrid systems deployed from edge to cloud. This paper reviews new architectures and technologies that enable containerized robotic applications to seamlessly run at the edge or in the cloud. We also overview systems that include solutions from extension to ROS 2 tooling to the integration of Kubernetes and ROS 2. Another important trend is robot learning, and how new simulators and cloud simulations are enabling, e.g., large-scale reinforcement learning or distributed federated learning solutions. This has also enabled deeper integration of continuous interaction and continuous deployment (CI/CD) pipelines for robotic systems development, going beyond standard software unit tests with simulated tests to build and validate code automatically. We discuss the current technology readiness and list the potential new application scenarios that are becoming available. Finally, we discuss the current challenges in distributed robotic systems and list open research questions in the field.

关键词： Multi-access edge computing Federated learning Pipelines Computer architecture Reinforcement learning Market research Robot learning

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Secure Heterogeneous Multi-Robot Collaboration and Docking with Hyperledger Fabric Blockchain

Secure Heterogeneous Multi-Robot Collaboration and Docking w...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Salma Salimi Paola Torrico Morón Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

In recent years, multi-robot systems have received increasing attention from both industry and academia. Besides the need of accurate and robust estimation of relative localization, security and trust in the system are essential to enable wider adoption. In this paper, we propose a framework using Hyperledger Fabric for multi-robot collaboration in industrial applications. We rely on blockchain identities for the interaction of ground and aerial robots, and use smart contracts for collaborative decision making. The use of ultra-wideband (UWB) localization for both autonomous navigation and robot collaboration extends our previous work in Fabric-based fleet management. We focus on an inventory management application which uses a ground robot and an aerial robot to inspect a warehouse-like environment and store information about the found objects in the blockchain. We measure the impact of adding the blockchain layer, analyze the transaction commit latency and compare the resource utilization of blockchain-related processes to the already running data processing modules.

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Benchmarking UWB-Based Infrastructure-Free Positioning and Multi-Robot Relative Localization: Dataset and Characterization

Benchmarking UWB-Based Infrastructure-Free Positioning and M...

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IEEE Sensors Applications Symposium, SAS

作者： Paola Torrico Morón Sahar Salimpour Lei Fu Xianjia Yu Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

Ultra-wideband (UWB) positioning has emerged as a low-cost and dependable localization solution for multiple use cases, from mobile robots to asset tracking within the Industrial IoT. The technology is mature and the scientific literature contains multiple datasets and methods for localization based on fixed UWB nodes. At the same time, research in UWB-based relative localization and infrastructure-free localization is gaining traction, further domains. tools and datasets in this domain are scarce. Therefore, we introduce in this paper a novel dataset for benchmarking infrastructure-free relative localization targeting the domain of multi-robot systems. Compared to previous datasets, we analyze the performance of different relative localization approaches for a much wider variety of scenarios with varying numbers of fixed and mobile nodes. A motion capture system provides ground truth data, are multi-modal and include inertial or odometry measurements for benchmarking sensor fusion methods. Additionally, the dataset contains measurements of ranging accuracy based on the relative orientation of antennas and a comprehensive set of measurements for ranging between a single pair of nodes. Our experimental analysis shows that mean accuracies of 10-20 cm are achievable with relatively simple methods; however, the positioning error can easily increase by an order of magnitude in degenerate scenarios, for which we provide different examples.

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Event-Driven Fabric Blockchain - ROS 2 Interface: Towards Secure and Auditable Teleoperation of Mobile Robots

Event-Driven Fabric Blockchain - ROS 2 Interface: Towards Se...

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International Conference on Ubiquitous Robots (UR)

作者： Lei Fu Salma Salimi Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

The integration of blockchain technology in robotic systems has been met by the community with a combination of hype and skepticism. The current literature shows that there is indeed potential for more secure and trustable distributed robotic systems. However, it is still unclear in what aspects of robotics beyond high-level decision making can blockchain technology be indeed usable. This paper explores the limits of a permissioned blockchain framework, Hyperledger Fabric, for teleoperation. Remote operation of mobile robots can benefit from the auditability and security properties of a blockchain. We study the potential benefits and the main limitations of such an approach. We introduce a new design and implementation for a event-driven Fabric-ROS 2 bridge that is able to maintain lower latencies at higher network loads than previous solutions. We also show this opens the door to more realistic use cases and applications. Our experiments with small aerial robots show latencies in the hundreds of milliseconds and the ability to remotely control both a single and multi-robot system. We analyze the main trade-offs and limitations for real-world near real-time remote teleoperation.

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Towards Large-Scale Relative Localization in Multi-Robot systems with Dynamic UWB Role Allocation

Towards Large-Scale Relative Localization in Multi-Robot Sys...

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International Conference on robotics and Automation Engineering (ICRAE)

作者： Paola Torrico Morón Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

Ultra-wideband (UWB) ranging has emerged as a key radio technology for robot positioning and relative localization in multi-robot systems. Multiple works are now advancing towards more scalable systems, but challenges still remain. This paper proposes a novel approach to relative localization in multi-robot systems where the roles of the UWB nodes are dynamically allocated between active nodes (using time-of-flight for ranging estimation to other active nodes) and passive nodes (using time-difference-of-arrival for estimating range differences with respect to pairs of active nodes). We adaptively update UWB roles based on the location of the robots with respect to the convex envelope defined by active nodes, and introducing constraints in the form of localization frequency and accuracy requirements. We demonstrate the applicability of the proposed approach and show that the localization errors remain comparable to fixed-role systems. Then, we show how the navigation of an autonomous drone is affected by the changes in the localization system, obtaining significantly better trajectory tracking accuracy than when relying in passive localization only. Our results pave the way for UWB-based localization in large-scale multi-robot deployments, for either relative positioning or for applications in GNSS-denied environments.

关键词： Location awareness Trajectory tracking Navigation Dynamic scheduling Autonomous aerial vehicles Distance measurement Multi-robot systems

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Distributed Ledger Technologies for Managing Heterogenous Computing systems at the Edge

Distributed Ledger Technologies for Managing Heterogenous Co...

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International Conference on Internet of Things: systems, Management and Security

作者： Daniel Montero Hernández Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

The increased use of Internet of Things (IoT) devices —from basic sensors to robust embedded computers— has boosted the demand for information processing and storing solutions closer to these devices. Edge computing has been established as a standard architecture for developing IoT solutions, since it can optimize the workload and capacity of systems that depend on cloud services by deploying necessary computing power close to where the information is being produced and consumed. However, as the network scale in size, reaching consensus becomes an increasingly challenging task. Distributed ledger technologies (DLTs), which can be described as a network of distributed databases that incorporate cryptography, can be leveraged to achieve consensus among participants. In recent years DLTs have gained traction due to the popularity of blockchains, the most-well known type of implementation. The reliability and trust that can be achieved through transparent and traceable transactions are other key concepts that bring IoT and DLT together. We present the design, development and conducted experiments of a proof-of-concept system that uses DLT smart contracts for efficiently selecting edge nodes for offloading computational tasks. In particular, we integrate network performance indicators in smart contracts with a Hyperledger Blockchain to optimize the offloading on computation under dynamic connectivity solutions. The proposed method can be applied to networks with varied topologies and different means of connectivity. Our results show the applicability of blockchain smart contracts to a variety of industrial use cases.

关键词： Distributed ledger Smart contracts Fabrics Peer-to-peer computing Blockchains Virtual private networks Topology

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Federated Learning for Vision-based Obstacle Avoidance in the Internet of robotic Things

Federated Learning for Vision-based Obstacle Avoidance in th...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Xianjia Yu Jorge Peña Queralta Tomi Westerlund Turku Intelligent Embedded and Robotic Systems (TIERS) Lab University of Turku Finland

Deep learning methods have revolutionized mobile robotics, from advanced perception models for an enhanced situational awareness to novel control approaches through re-inforcement learning. This paper explores the potential of federated learning for distributed systems of mobile robots enabling collaboration on the Internet of robotic Things. To demonstrate the effectiveness of such an approach, we deploy wheeled robots in different indoor environments. We analyze the performance of a federated learning approach and compare it to a traditional centralized training process with a priori aggregated data. We show the benefits of collaborative learning across heterogeneous environments and the potential for sim-to-real knowledge transfer. Our results demonstrate significant performance benefits of FL and sim-to-real transfer for vision-based navigation, in addition to the inherent privacy-preserving nature of FL by keeping computation at the edge. This is, to the best of our knowledge, the first work to leverage FL for vision-based navigation that also tests results in real-world settings.

关键词： Training Deep learning Multi-access edge computing Federated learning Navigation Internet Indoor environment

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Evaluating the Performance of Multi-Scan Integration for UAV LiDAR-based Tracking

arXiv

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arXiv 2023年

作者： Catalano, Iacopo Queralta, Jorge Peña Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems University of Turku Finland

Drones have become essential tools in a wide range of industries, including agriculture, surveying, and transportation. However, tracking unmanned aerial vehicles (UAVs) in challenging environments, such cluttered or GNSS-denied environments, remains a critical issue. Additionally, UAVs are being deployed as part of multi-robot systems, where tracking their position can be essential for relative state estimation. In this paper, we evaluate the performance of a multi-scan integration method for tracking UAVs in GNSS-denied environments using a solid-state LiDAR and a Kalman Filter (KF). We evaluate the algorithm’s ability to track a UAV in a large open area at various distances and speeds. Our quantitative analysis shows that while"tracking by detection" using a Constant Velocity model is the only method that consistently tracks the target, integrating multiple scan frequencies using a KF achieves lower position errors and represents a viable option for tracking UAVs in similar scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

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Decentralized Vision-Based Byzantine Agent Detection in Multi-Robot systems with IOTA Smart Contracts

arXiv

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arXiv 2022年

作者： Salimpour, Sahar Keramat, Farhad Queralta, Jorge Peña Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems University of Turku Finland

Multiple opportunities lie at the intersection of multi-robot systems and distributed ledger technologies (DLTs). In this work, we investigate the potential of new DLT solutions such as IOTA, for detecting anomalies and byzantine agents in multi-robot systems in a decentralized manner. Traditional blockchain approaches are not applicable to real-world networked and decentralized robotic systems where connectivity conditions are not ideal. To address this, we leverage recent advances in partition-tolerant and byzantine-tolerant collaborative decision-making processes with IOTA smart contracts. We show how our work in vision-based anomaly and change detection can be applied to detecting byzantine agents within multiple robots operating in the same environment. We show that IOTA smart contracts add a low computational overhead while allowing to build trust within the multi-robot system. The proposed approach effectively enables byzantine robot detection based on the comparison of images submitted by the different robots and detection of anomalies and changes between them. © 2022, CC BY.

关键词： Computer vision

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