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International Conference on control, Automation and systems ( ICCAS)

作者： P. Mäkinen P. Mustalahti S. Launis J. Mattila Automation Technology and Mechanical Engineering Tampere University Tampere Finland Rock Technologies and Drilling Sandvik Mining and Construction Oy Tampere Finland

In this paper, a model-free data fusion method for combining redundant sensor data is presented. The objective is to maintain a reliable tool center point pose measurement of a long-reach robotic manipulator using a visual sensor system with multiple cameras. The fusion method is based on weighted averaging. The weight parameter for each variable is computed using the sliding window variance with N latest observations. After each sliding window, the window length N is updated, and simple transition smoothing is included. For experimental validation, two sets of pose trajectory data from redundant visual sensors were obtained: 1) using a camera located near the tip of a long-reach manipulator running a simultaneous localization and mapping (SLAM) algorithm and 2) marker-based tracking with cameras located near the base of the manipulator. For pose tracking, a fiducial marker was attached near the SLAM camera. The proposed methodology was examined using a real-time measurement setup and offline data analysis using the recorded data. The results demonstrate that the proposed system can increase the overall robustness and fault tolerance of the system, which are desired features for future autonomous field robotic machines.

关键词： Visualization Simultaneous localization and mapping Smoothing methods Robot vision systems Fault tolerant systems Cameras Manipulators

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Localization of the autonomous mobile robot based on sensor fusion

Localization of the autonomous mobile robot based on sensor ...

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IEEE International Symposium on Intelligent control (ISIC)

作者： Lei Cheng Yongji Wang Center for Nonlinear and Complex System Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

This paper emphasizes on the sensor fusion algorithm on mobile robot's localization, to solve the problem of accumulated error attributed to wheel encoders and redundant readings in multi-sonar system. Based on the error analysis of wheel encoders and sonar, a new integrated intelligent sensor fusion algorithm is introduced, the novel core of such algorithm is error judgement rule. With this technique, the mobile robot can autonomously correct its position in proper time. As demonstration, an experiment on the Pioneer II mobile robot has been completed. Pioneer II is an intelligent mobile robotic platform manufactured by ActivMedia Corporation, it is equipped with an internal wheel encoders and a full-span sonar ring. The experiment result shows that such algorithm not only avoids the error accumulation with regard to wheel encoders, but also guarantees the motion speed of robot.

关键词： Mobile robots sensor fusion Wheels Sonar Robot kinematics Intelligent sensors Intelligent robots Signal processing algorithms Robot sensing systems Error analysis

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

6th International Conference on Cognitive Systems and Signal...

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

ISBN: (纸本)9789811692468

The proceedings contain 41 papers. The special focus in this conference is on Cognitive systems and Signal Processing. The topics include: Functional Primitive Library and Movement Sequence Reasoning Algorithm;constrained control for systems on Lie Groups with Uncertainties via Tube-Based Model Predictive control on Euclidean Spaces;social-Transformer: Pedestrian Trajectory Prediction in autonomous Driving Scenes;gridPointNet: Grid and Point-Based 3D Object Detection from Point Cloud;depth Image Super-resolution via Two-Branch Network;EBANet: Efficient Boundary-Aware Network for RGB-D Semantic Segmentation;camouflaged Object Segmentation with Transformer;DGrid: Dense Grid Network for Salient Object Detection;a Multi-frame Lane Detection Method Based on Deep Learning;unsupervised Semantic Segmentation with Contrastive Translation Coding;ensemble Deep Learning Based Single Finger-Vein Recognition;hand-Dorsa Vein Recognition Based on Local Deep Feature;Detection Method of Apple Based on Improved Lightweight YOLOv5;scene Graph Prediction with Concept Knowledge Base;a Discussion of Data Sampling Strategies for Early Action Prediction;sensor fusion Based Weighted Geometric Distance Data Association Method for 3D Multi-object Tracking;multiple Granularities with Gradual Transition Network for Person Re-identification;generative Adversarial Networks and Improved Efficientnet for Imbalanced Diabetic Retinopathy Grading;sample-Efficient Reinforcement Learning Based on Dynamics Models via Meta-policy Optimization;from Human Oral Instructions to General Representations of Knowledge: A New Paradigm for Industrial Robots Skill Teaching;multi-class Feature Selection Based on Softmax with L2, 0 -Norm Regularization;3D Grasping Pose Detection Method Based on Improved PointNet Network;MCTS-Based robotic Exploration for Scene Graph Generation;predictive Maintenance Estimation of Aircraft Health with Survival Analysis.

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Force, acceleration and vision sensor fusion for contour following tasks with an industrial robot

Force, acceleration and vision sensor fusion for contour fol...

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International Workshop on Robot Sensing (ROSE)

作者： Heiko Koch Alexander König Alexandra Weigl-Seitz Karl Kleinmann Jozef Suchý University of Applied Sciences Darmstadt Darmstadt Germany Chemnitz Technical University Chemnitz Germany

In robotic contour following tasks such as sewing or cutting an industrial robot guides a tool along a contour of a workpiece. Teaching the task manually is very time consuming and the process would be unable to react to uncertainties or changes of the environment. Thus sensor systems are applied to reduce human intervention, making the robot more autonomous. In this paper we present an integrated approach for multisensor contour following. A look-ahead vision sensor guides the tool along a contour of a workpiece while force feedback control maintains a desired contact force. Couplings that arise between these two controllers are estimated and compensated through online measurements of contact forces between robot and environment. The identification of the environment combines force and acceleration sensors in an observer-based control scheme. The system is validated by experiments that involve contour following on compliant objects.

关键词： Robot sensing systems Force Acceleration Force measurement Position measurement Visualization

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autonomous robot navigation based on fuzzy sensor fusion and reinforcement learning

Autonomous robot navigation based on fuzzy sensor fusion and...

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IEEE International Symposium on Intelligent control (ISIC)

作者： K.C. Tan K.K. Tan T.H. Lee S. Zhao Y.J. Chen Department of Electrical and Computer Engineering National University of Singapore Singapore

This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into a subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. Real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behavior such as target collecting in an ever-changing physical environment.

关键词： Robot sensing systems Navigation sensor fusion Learning Intelligent robots Intelligent sensors Target recognition Intelligent systems Fuzzy logic Working environment noise

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Taking control: Modular and adaptive robotics process control systems

Taking control: Modular and adaptive robotics process contro...

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International Workshop on Robot Sensing (ROSE)

作者： Peter Ulbrich Florian Franzmann Christian Harkort Martin Hoffmann Tobias Klaus Anja Rebhan Wolfgang Schröder-Preikschat Chair of Distributed Systems and Operating Systems Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Germany Chair of Automatic Control Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Germany Friedrich-Alexander-Universitat Erlangen-Nurnberg Erlangen Bayern DE

robotics systems usually comprise sophisticated sensor and actuator systems with no less complex control applications. These systems are subject to frequent modifications and extensions and have to adapt to their environment. While automation systems are tailored to particular production processes, autonomous vehicles must adaptively switch their sensors and controllers depending on environmental conditions. However, when designing and implementing the process control system, traditional control theory focuses on the control problem at hand without having this variability in mind. Thus, the resulting models and implementation artefacts are monolithic, additionally complicating the real-time system design. In this paper, we present a modularisation approach for the design of robotics process control systems, which not only aims for variability at design-time but also for adaptivity at run-time. Our approach is based on a layered control architecture, which includes an explicit interface between the two domains involved: control engineering and computer science. Our architecture provides separation of concerns in terms of independent building blocks and data flows. For example, the replacement of a sensor no longer involves the tedious modification of downstream filters and controllers. Likewise, the error-prone mapping of high-level application behaviour to the process control system can be omitted. We validated our approach by the example of an autonomous vehicle use case. Our experimental results demonstrate ease of use and the capability to maintain quality of control on par with the original monolithic design.

关键词： Observers sensor fusion Actuators sensor systems Real-time systems

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Swarm Underwater Acoustic 3D Localization: Kalman vs Monte Carlo

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INTERNATIONAL JOURNAL OF ADVANCED robotic systems 2015年第7期12卷 102-102页

作者： Taraglio, Sergio Fratichini, Fabio ENEA Ctr Ric Casaccia Rome Italy Univ Roma Tor Vergata Dept Elect Engn Rome Italy

Two three-dimensional localization algorithms for a swarm of underwater vehicles are presented. The first is grounded on an extended Kalman filter (EKF) scheme used to fuse some proprioceptive data such as the vessel's speed and some exteroceptive measurements such as the time of flight (TOF) sonar distance of the companion vessels. The second is a Monte Carlo particle filter localization processing the same sensory data suite. The results of several simulations using the two approaches are presented, with comparison. The case of a supporting surface vessel is also considered. An analysis of the robustness of the two approaches against some system parameters is given.

关键词： Distributed control systems Mobile Robots and Intelligent autonomous systems Underwater Robots sensor fusion Swarm Localization

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Model-based sensor fusion and filtering for localization of a semi-autonomous robotic vehicle*

Model-based sensor fusion and filtering for localization of ...

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European control Conference (ECC)

作者： Catalin Stefan Teodorescu Irving Caplan Harry Eberle Tom Carlson Aspire Create University College London Royal National Orthopaedic Hospital UK

This paper refines a physically-inspired model governing the dynamic motion of a vehicle. We present a method used to perform experimental parameter calibration, and then use this model to build an observer (an extended Kalman filter). Experimental results with a robotic vehicle fitted with a prototype kit focus on recovering the truthful real-world information in the context of systematic errors (a faulty wheel encoder sensor), randomly occurring errors (a faulty ultrasonic sensor) and simplifying model assumptions (e.g. usage of two identical motors). We show that our model-based approach is able to perform reasonably well even under these extreme circumstances.

关键词： Torque Systematics Estimation Wheels Observers Robot sensing systems Mobile robots

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sensor fusion and failure detection using virtual sensors

Sensor fusion and failure detection using virtual sensors

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American control Conference (ACC)

作者： T.W. Long E.L. Hanzevack W.L. Bynum College of William and Mary USA

Fault detection has become an integral aspect of modern system design. To minimize costs, a software approach is desirable. We have designed a neural network based decentralized virtual sensor system to conduct sensor fusion and sensor failure detection. To accomplish effective fault detection, we have conceived an innovative row-wise and column-wise checking procedure that is able to detect all sensor faults while minimizing false alarms. Another measure that reduces the damage of false alarms is the addition of a sensor reinstatement procedure. This allows us to further tighten the decision threshold to prevent missed detection without compromising the goal of false alarm deterrence. We have demonstrated this virtual sensor system using a vibrating beam structure. The system has been shown to be very robust, giving a 100% detection rate in all experiments. This virtual sensor concept can be applied to large structures, such as a space platform, where robustness and autonomous reconfiguration is essential.

关键词： sensor fusion Fault detection sensor systems Neural networks Costs Hardware sensor phenomena and characterization Frequency Educational institutions Robustness

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robotic ecology: Tracking small dynamic animals with an autonomous aerial vehicle

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SCIENCE roboticS 2018年第23期3卷 eaat8409页

作者： Cliff, Oliver M. Saunders, Debra L. Fitch, Robert Univ Sydney Australian Ctr Field Robot Sydney NSW Australia Australian Natl Univ Fenner Sch Canberra ACT Australia Univ Technol Sydney Ctr Autonomous Syst Sydney NSW Australia

Understanding animal movements that underpin ecosystem processes is fundamental to ecology. Recent advances in animal tags have increased the ability to remotely locate larger species;however, this technology is not suitable for up to 70% of the world's bird and mammal species. The most widespread technique for tracking small animals is to manually locate low-power radio transmitters from the ground with handheld equipment. Despite this labor-intensive technique being used for decades, efforts to reduce or automate this process have had limited success. Here, we present an approach for tracking small radio-tagged animals by using an autonomous and lightweight aerial robot. We present experimental results where we used the robot to locate critically endangered swift parrots (Lathamus discolor) within their winter range. The system combines a miniaturized sensor with newly developed estimation algorithms to yield unambiguous bearing- and range-based measurements with associated measures of uncertainty. We incorporated these measurements into Bayesian data fusion and information-based planning algorithms to control the position of the robot as it collected data. We report estimated positions that lie within about 50 meters of the true positions of the birds on average, which are sufficiently accurate for recapture or observation. Further, in comparison with experienced human trackers from locations where the signal was detectable, the robot produced a correct estimate as fast or faster than the human. These results provide validation of robotic systems for wildlife radio telemetry and suggest a way for widespread use as human-assistive or autonomous devices.

关键词： Mammals

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