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Canadian Conference on Computer and Robot Vision, CRV

作者： Jun Zhou C.M. Clark Lab for Autonomous and Intelligent Robotics Department of Mechanical Engineering University of Waterloo Waterloo ONT Canada

This paper concerns the autonomous tracking of fish using a Remotely Operated Vehicle (ROV) equipped with a single camera. An efficient image processing algorithm is presented that enables pose estimation of a particular species of fish - a Large Mouth Bass. The algorithm uses a series of filters including the Gabor filter for texture, projection segmentation, and geometrical shape feature extraction to find the fishes distinctive dark lines that mark the body and tail. Feature based scaling then produces the position and orientation of the fish relative to the ROV. By implementing this algorithm on each frame of a series of video frames, successive relative state estimates can be obtained which are fused across time via a Kalman Filter. Video taken from a VideoRay MicroROV operating within Paradise Lake, Ontario, Canada was used to demonstrate off-line fish state estimation. In the future, this approach will be integrated within a closed-loop controller that allows the robot to autonomously follow the fish and monitor its behavior.

关键词： Marine animals Remotely operated vehicles Cameras Gabor filters State estimation Image processing Mouth Image segmentation Shape Feature extraction

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Adaptive control strategies for open-loop dynamic hopping

Adaptive control strategies for open-loop dynamic hopping

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2009 IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Marco Hutter C. David Remy Roland Siegwart Autonomous Systems Lab Institute of Robotics and Intelligent Systems Swiss Federal Institute of Technology Zurich Switzerland

In the present study, we investigate a control strategy for hopping motions of an articulated leg that is driven by series elastic actuation. A highly compliant spring in the knee joint allows the exploitation of periodic energy storage but creates a major control challenge by severely limiting the bandwidth of closed-loop position or force control. This handicap is intensified by slow actuators, substantial delays, and the kinematic coupling of the articulated design. With classic closed-loop control strategies failing, an adaptive open-loop control algorithm is presented, that, over a series of jumps, estimates the compression of the actuator springs, and gradually modifies the motor inputs in order to minimize slipping and create a purely vertical motion.

关键词： Adaptive control Open loop systems Motion control Springs Force control Actuators Leg Knee Energy storage Bandwidth

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Robust Sliding Mode Based Finite-time Bilateral Shared Teleoperation System with Unsymmetrical Time-Varying Delay* 48

Robust Sliding Mode Based Finite-time Bilateral Shared Teleo...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Islam, Shafiqul Sunda-Meya, Anderson Xavier University of Louisiana Dual Degree Engineering Program Intelligent Robotics Mechatronics & Autonomous Systems Lab New Orleans70125 United States Xavier University of Louisiana College of Arts & Sciences New Orleans70125 United States

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This paper presents sliding mode-based finite-time synchronization algorithms for bilateral shared teleoperation systems under unsymmetrical time-varying delay and uncertainty. The synchronization algorithms are designed by using Lyapunov and sliding mode control theory. First, the model-based finite-time synchronization algorithms are designed by using sliding mode control theory with the presence of time-varying delays and uncertainty. Then, robust adaptive finite-time synchronization algorithms are designed for bilateral shared teleoperation systems with the presence of time-varying delays. Adaptive learning algorithms learn and adapt with uncertainty associated with the unmodeled dynamics and external disturbances. Lyapunov analysis shows that the tracking errors between master and slave manipulators converge to the sliding surface in finite time. It also shows that the tracking errors asymptotically converge to zero on the sliding surface. The design provides finite-time convergence of the states to reach the sliding surface as opposed to asymptotic convergence-based bilateral shared teleoperation systems. The design and analysis do not use the exact bound of the uncertainty. In contrast with asymptotic design, finite-time convergence can ensure faster and more robust tracking in the presence of uncertainty and time-varying delays. Unlike reported designs, the proposed algorithms can ensure convergence with the presence of unsymmetrical time-varying delays. © 2022 IEEE.

关键词： Time delay

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Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching

Beyond Photometric Consistency: Gradient-based Dissimilarity...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jan Quenzel Radu Alexandru Rosu Thomas Läbe Cyrill Stachniss Sven Behnke Autonomous Intelligent Systems Group University of Bonn Germany Robotics and Photogrammetry Lab University of Bonn Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Pose estimation and map building are central ingredients of autonomous robots and typically rely on the registration of sensor data. In this paper, we investigate a new metric for registering images that builds upon on the idea of the photometric error. Our approach combines a gradient orientation-based metric with a magnitude-dependent scaling term. We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric. Our experimental evaluation indicate that our metric leads to more robust and more accurate estimates of the scene depth as well as camera trajectory. Thus, the metric improves camera pose estimation and in turn the mapping capabilities of mobile robots. We believe that a series of existing visual odometry and visual SLAM systems can benefit from the findings reported in this paper.

关键词： Measurement Robustness Cameras Estimation Visual odometry Simultaneous localization and mapping Robot vision systems

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Learning multiple collaborative tasks with a mixture of Interaction Primitives

Learning multiple collaborative tasks with a mixture of Inte...

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2015 IEEE International Conference on robotics and Automation, ICRA 2015

作者： Ewerton, Marco Neumann, Gerhard Lioutikov, Rudolf Amor, Heni Ben Peters, Jan Maeda, Guilherme Intelligent Autonomous Systems Lab Department of Computer Science Technische Universität Darmstadt Hochschulstr. 10 Darmstadt64289 Germany Institute of Robotics and Intelligent Machines Georgia Institute of Technology 801 Atlantic Drive AtlantaGA30332-0280 United States Max Planck Institute for Intelligent Systems Spemannstr. 38 Tuebingen72076 Germany

ISBN: (纸本)9781479969234

Robots that interact with humans must learn to not only adapt to different human partners but also to new interactions. Such a form of learning can be achieved by demonstrations and imitation. A recently introduced method to learn interactions from demonstrations is the framework of Interaction Primitives. While this framework is limited to represent and generalize a single interaction pattern, in practice, interactions between a human and a robot can consist of many different patterns. To overcome this limitation this paper proposes a Mixture of Interaction Primitives to learn multiple interaction patterns from unlabeled demonstrations. Specifically the proposed method uses Gaussian Mixture Models of Interaction Primitives to model nonlinear correlations between the movements of the different agents. We validate our algorithm with two experiments involving interactive tasks between a human and a lightweight robotic arm. In the first, we compare our proposed method with conventional Interaction Primitives in a toy problem scenario where the robot and the human are not linearly correlated. In the second, we present a proof-of-concept experiment where the robot assists a human in assembling a box. © 2015 IEEE.

关键词： Demonstrations

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Passive dynamic walking with quadrupeds - Extensions towards 3D

Passive dynamic walking with quadrupeds - Extensions towards...

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IEEE International Conference on robotics and Automation (ICRA)

作者： C. David Remy Marco Hutter Roland Siegwart Autonomous Systems Lab Institute of Robotics and Intelligent Systems Swiss Federal Institute of Technology ETH Zurich Zurich Switzerland

In the present study, we applied the principles of passive dynamic walking onto the three dimensional motion of a simplified quadrupedal model. We extended the simulation framework of a planar system to include a rolling degree of freedom and searched for limit cycles that represent periodic gaits. Among the eight different gaits that we identified, were three kinds of trots and paces, as well as a lateral and diagonal single foot sequence. We could show that a distinct relation exists between the lateral spacing of the legs and the relative phase of the front and the back legs, and a certain trade-off between efficiency and dynamic stability. In agreement with established bipedal models, our results showed that the lateral rolling motion is invariably unstable.

关键词： Legged locomotion Leg Foot Limit-cycles Humans intelligent robots robotics and automation USA Councils Stability Couplings

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Overlapnet: Loop closing for LiDAR-based SLAM

arXiv

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arXiv 2021年

作者： Chen, Xieyuanli Läbe, Thomas Milioto, Andres Röhling, Timo Vysotska, Olga Haag, Alexandre Behley, Jens Stachniss, Cyrill Photogrammetry & Robotics Lab University of Bonn Germany Fraunhofer FKIE Wachtberg Germany Autonomous Intelligent Driving GmbH Munich Germany

Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data for finding loop closures. It estimates an image overlap generalized to range images and provides a relative yaw angle estimate between pairs of scans. Based on such predictions, we tackle loop closure detection and integrate our approach into an existing SLAM system to improve its mapping results. We evaluate our approach on sequences of the KITTI odometry benchmark and the Ford campus dataset. We show that our method can effectively detect loop closures surpassing the detection performance of state-of-the-art methods. To highlight the generalization capabilities of our approach, we evaluate our model on the Ford campus dataset while using only KITTI for training. The experiments show that the learned representation is able to provide reliable loop closure candidates, also in unseen environments. © 2021, CC BY.

关键词： Deep neural networks

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Collision-Free Volume Estimation Algorithm for Robot Motion Deformation

Collision-Free Volume Estimation Algorithm for Robot Motion ...

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International Conference on Advanced robotics (ICAR)

作者： Nicola Miotto Alberto Gottardi Nicola Castaman Emanuele Menegatti Department of Information Engineering Intelligent Autonomous System Lab University of Padova Padua Italy IT+Robotics srl Vicenza Italy

The collaborative transport of objects between humans and robots is one of the main areas of focus in physical Human-Robot Interaction (pHRI). Ensuring the operator's safety and maintaining collision-free motion of the robot during transportation are crucial challenges in this context. Consider a collaborative co-manipulation scenario where the operator modifies the trajectory being executed by the robot. In such cases, the robot may deviate from its previously calculated path, potentially resulting in collisions. In this work, we propose a method to estimate the maximum collision-free volume around the path of the robot. This volume represents the permissible deviation introduced by the human worker while ensuring that no collisions occur. To evaluate the effectiveness of the proposed algorithm, we test it in a real industrial scenario.

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Learning Human Objectives from Sequences of Physical Corrections

Learning Human Objectives from Sequences of Physical Correct...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mengxi Li Alper Canberk Dylan P. Losey Dorsa Sadigh Intelligent and Interactive Autonomous Systems Group (ILIAD) Stanford University Stanford CA Collaborative Robotics Lab (Collab) Virginia Tech

When personal, assistive, and interactive robots make mistakes, humans naturally and intuitively correct those mistakes through physical interaction. In simple situations, one correction is sufficient to convey what the human wants. But when humans are working with multiple robots or the robot is performing an intricate task often the human must make several corrections to fix the robot’s behavior. Prior research assumes each of these physical corrections are independent events, and learns from them one-at-a-time. However, this misses out on crucial information: each of these interactions are interconnected, and may only make sense if viewed together. Alternatively, other work reasons over the final trajectory produced by all of the human’s corrections. But this method must wait until the end of the task to learn from corrections, as opposed to inferring from the corrections in an online fashion. In this paper we formalize an approach for learning from sequences of physical corrections during the current task. To do this we introduce an auxiliary reward that captures the human’s trade-off between making corrections which improve the robot’s immediate reward and long-term performance. We evaluate the resulting algorithm in remote and in-person human-robot experiments, and compare to both independent and final baselines. Our results indicate that users are best able to convey their objective when the robot reasons over their sequence of corrections.

关键词： Automation Conferences Human-robot interaction Programming Robot sensing systems Trajectory Task analysis

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A modular robotic arm control stack for research: Franka-interface and frankapy

arXiv

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arXiv 2020年

作者： Zhang, Kevin Sharma, Mohit Liang, Jacky Kroemer, Oliver Intelligent Autonomous Manipulation Lab Robotics Institute Carnegie Mellon University 5000 Forbes Ave PittsburghPA United States

We designed a modular robotic control stack that provides a customizable and accessible interface to the Franka Emika Panda Research robot. This framework abstracts high-level robot control commands as skills, which are decomposed into combinations of trajectory generators, feedback controllers, and termination handlers. Low-level control is implemented in C++ and runs at 1kHz, and high-level commands are exposed in Python. In addition, external sensor feedback, like estimated object poses, can be streamed to the low-level controllers in real time. This modular approach allows us to quickly prototype new control methods, which is essential for research applications. We have applied this framework across a variety of real-world robot tasks in more than 5 published research papers. The framework is currently shared internally with other robotics labs at Carnegie Mellon University, and we plan for a public release in the near future. Copyright © 2020, The Authors. All rights reserved.

关键词： Controllers

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