检索结果-内蒙古大学图书馆

CIDGIKc: Distance-Geometric Inverse Kinematics for Continuum Robots

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhang, Hanna Jiamei Giamou, Matthew Marić, Filip Kelly, Jonathan Burgner-Kahrs, Jessica Continuum Robotics Laboratory Department of Mathematical & Computational Sciences University of Toronto Canada Space & Terrestrial Autonomous Robotic System Laboratory Institute for Aerospace Studies University of Toronto Canada

The small size, high dexterity, and intrinsic compliance of continuum robots (CRs) make them well suited for constrained environments. Solving the inverse kinematics (IK), that is finding robot joint configurations that satisfy desired position or pose queries, is a fundamental challenge in motion planning, control, and calibration for any robot structure. For CRs, the need to avoid obstacles in tightly confined workspaces greatly complicates the search for feasible IK solutions. Without an accurate initialization or multiple re-starts, existing algorithms often fail to find a solution. We present CIDGIKc (Convex Iteration for Distance-Geometric Inverse Kinematics for Continuum Robots), an algorithm that solves these nonconvex feasibility problems with a sequence of semidefinite programs whose objectives are designed to encourage low-rank minimizers. CIDGIKc is enabled by a novel distance-geometric parameterization of constant curvature segment geometry for CRs with extensible segments. The resulting IK formulation involves only quadratic expressions and can efficiently incorporate a large number of collision avoidance constraints. Our experimental results demonstrate >98% solve success rates within complex, highly cluttered environments which existing algorithms cannot account for. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse kinematics

Generative Graphical Inverse Kinematics

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Limoyo, Oliver Marić, Filip Giamou, Matthew Alexson, Petra Petrović, Ivan Kelly, Jonathan Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

Quickly and reliably finding accurate inverse kinematics (IK) solutions remains a challenging problem for many robot manipulators. Existing numerical solvers are broadly applicable but typically only produce a single solution and rely on local search techniques to minimize nonconvex objective functions. More recent learning-based approaches that approximate the entire feasible set of solutions have shown promise as a means to generate multiple fast and accurate IK results in parallel. However, existing learning-based techniques have a significant drawback: each robot of interest requires a specialized model that must be trained from scratch. To address this key shortcoming, we propose a novel distance-geometric robot representation coupled with a graph structure that allows us to leverage the sample efficiency of Euclidean equivariant functions and the generalizability of graph neural networks (GNNs). Our approach is generative graphical inverse kinematics (GGIK), the first learned IK solver able to accurately and efficiently produce a large number of diverse solutions in parallel while also displaying the ability to generalize—a single learned model can be used to produce IK solutions for a variety of different robots. When compared to several other learned IK methods, GGIK provides more accurate solutions with the same amount of data. GGIK can generalize reasonably well to robot manipulators unseen during training. Additionally, GGIK can learn a constrained distribution that encodes joint limits and scales efficiently to larger robots and a high number of sampled solutions. Finally, GGIK can be used to complement local IK solvers by providing reliable initializations for a local optimization process. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse kinematics

Spatiotemporal Calibration of 3D Millimetre-Wavelength Radar-Camera Pairs

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wise, Emmett Cheng, Qilong Kelly, Jonathan The Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada

autonomous vehicles (AVs) often depend on multiple sensors and sensing modalities to impart a measure of robustness when operating in adverse conditions. Radars and cameras are popular choices for use in combination;although radar measurements are sparse in comparison to camera images, radar scans are able to penetrate fog, rain, and snow. Data from both sensors are typically fused prior to use in downstream perception tasks. However, accurate sensor fusion depends upon knowledge of the spatial transform between the sensors and any temporal misalignment that exists in their measurement times. During the life cycle of an AV, these calibration parameters may change, so the ability to perform in-situ spatiotemporal calibration is essential to ensure reliable long-term operation. State-of-the-art 3D radar-camera spatiotemporal calibration algorithms require bespoke calibration targets that are not readily available in the field. In this paper, we describe an algorithm for targetless spatiotemporal calibration that is able to operate without specialized infrastructure. Our approach leverages the ability of the radar unit to measure its own ego-velocity relative to a fixed, external reference frame. We analyze the identifiability of the spatiotemporal calibration problem and determine the motions necessary for calibration. Through a series of simulation studies, we characterize the sensitivity of our algorithm to measurement noise. Finally, we demonstrate accurate calibration for three real-world systems, including a handheld sensor rig and a vehicle-mounted sensor array. Our results show that we are able to match the performance of an existing, target-based method, while calibrating in arbitrary, infrastructure-free environments. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic Calibration

学校读者我要写书评

暂无评论

A Continuous-Time Approach for 3D Radar-to-Camera Extrinsic ...

IEEE International Conference on robotics and Automation (ICRA)

作者： Emmett Wise Juraj Peršić Christopher Grebe Ivan Petrović Jonathan Kelly Space & Terrestrial Autonomous Robotics Systems (STARS) Laboratory The University of Toronto Institute for Aerospace Studies Toronto Canada Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Croatia

Reliable operation in inclement weather is essential to the deployment of safe autonomous vehicles (AVs). Robustness and reliability can be achieved by fusing data from the standard AV sensor suite (i.e., lidars, cameras) with weather robust sensors, such as millimetre-wavelength radar. Critically, accurate sensor data fusion requires knowledge of the rigidbody transform between sensor pairs, which can be determined through the process of extrinsic calibration. A number of extrinsic calibration algorithms have been designed for 2D (planar) radar sensors—however, recently-developed, low-cost 3D millimetre-wavelength radars are set to displace their 2D counterparts in many applications. In this paper, we present a continuous-time 3D radar-to-camera extrinsic calibration algorithm that utilizes radar velocity measurements and, unlike the majority of existing techniques, does not require specialized radar retroreflectors to be present in the environment. We derive the observability properties of our formulation and demonstrate the efficacy of our algorithm through synthetic and real-world experiments.

关键词： Three-dimensional displays Radar measurements Radar Transforms Cameras Calibration Trajectory

Riemannian Optimization for Distance-Geometric Inverse Kinematics

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Marić, Filip Giamou, Matthew Hall, Adam W. Khoubyarian, Soroush Petrović, Ivan Kelly, Jonathan Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Dynamic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada

Solving the inverse kinematics problem is a fundamental challenge in motion planning, control, and calibration for articulated robots. Kinematic models for these robots are typically parametrized by joint angles, generating a complicated mapping between the robot configuration and the end-effector pose. Alternatively, the kinematic model and task constraints can be represented using invariant distances between points attached to the robot. In this paper, we formalize the equivalence of distance-based inverse kinematics and the distance geometry problem for a large class of articulated robots and task constraints. Unlike previous approaches, we use the connection between distance geometry and low-rank matrix completion to find inverse kinematics solutions by completing a partial Euclidean distance matrix through local optimization. Furthermore, we parametrize the space of Euclidean distance matrices with the Riemannian manifold of fixed-rank Gram matrices, allowing us to leverage a variety of mature Riemannian optimization methods. Finally, we show that bound smoothing can be used to generate informed initializations without significant computational overhead, improving convergence. We demonstrate that our inverse kinematics solver achieves higher success rates than traditional techniques, and substantially outperforms them on problems that involve many workspace constraints. Copyright © 2021, The Authors. All rights reserved.

关键词： Motion planning

Gaussian Processes Incremental Inference for Mobile Robots Dynamic Planning ⁎

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2020年第2期53卷 9584-9589页

作者： Luka Petrović Filip Marić Ivan Marković Ivan Petrović University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Institute for Aerospace Studies Space & Terrestrial Autonomous Robotic Systems Laboratory Canada

Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. While such methods efficiently find solutions in static environments, they need to be ran from scratch multiple times in the presence of moving obstacles, which incurs unnecessary computation and slows down execution. In this paper, we propose a trajectory optimization algorithm that anticipates the movement of obstacles and solves the planning problem in an iterative manner. We employ continuous-time Gaussian processes as trajectory representations both for the mobile robot and moving obstacles for which future locations are predicted according to a given model. We formulate the simultaneous moving obstacles tracking and mobile robot motion planning problem as probabilistic inference on a factor graph. Since trajectories of moving obstacles are optimized concurrently to motion planning, the proposed approach works in a predictive manner. After computing the initial solution, we use incremental inference for online replanning after an estimate of the moving obstacle position is provided. Our experimental evaluation demonstrates that the proposed approach supports online motion generation in the presence of moving obstacles.

关键词： motion planning trajectory optimization gaussian processes factor graphs incremental inference

Learning a Stability Filter for Uncertain Differentially Flat systems using Gaussian Processes

学校读者我要写书评

暂无评论

Learning a Stability Filter for Uncertain Differentially Fla...

IEEE Conference on Decision and Control

作者： Melissa Greeff Adam W. Hall Angela P. Schoellig Dynamic Systems Lab (***) University of Toronto Institute for Aerospace Studies (UTIAS) and the Vector Institute for Artificial Intelligence Toronto Space and Terrestrial Autonomous Robotic Systems Laboratory

ISBN: (纸本)9781665436601

Many physical system models exhibit a structural property known as differential flatness. Intuitively, differential flatness allows us to separate the system’s nonlinear dynamics into a linear dynamics component and a nonlinear term. In this work, we exploit this structure and propose using a nonparametric Gaussian Process (GP) to learn the unknown nonlinear term. We use this GP in an optimization problem to optimize for an input that is most likely to feedback linearize the system (i.e., cancel this nonlinear term). This optimization is subject to input constraints and a stability filter, described by an uncertain Control Lyapunov Function (CLF), which probabilistically guarantees exponential trajectory tracking when possible. Furthermore, for systems that are control-affine, we choose to express this structure in the selection of the kernel for the GP. By exploiting this selection, we show that the optimization problem is not only convex but can be efficiently solved as a second-order cone program. We compare our approach to related works in simulation and show that we can achieve similar performance at much lower computational cost.

关键词： Uncertainty Trajectory tracking Computational modeling Gaussian processes Control systems Stability analysis Computational efficiency

Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization

学校读者我要写书评

暂无评论

Inverse Kinematics for Serial Kinematic Chains via Sum of Sq...

IEEE International Conference on robotics and Automation (ICRA)

作者： Filip Marić Matthew Giamou Soroush Khoubyarian Ivan Petrović Jonathan Kelly University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems Laboratory Canada University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Inverse kinematics is a fundamental challenge for articulated robots: fast and accurate algorithms are needed for translating task-related workspace constraints and goals into feasible joint configurations. In general, inverse kinematics for serial kinematic chains is a difficult nonlinear problem, for which closed form solutions cannot easily be obtained. Therefore, computationally efficient numerical methods that can be adapted to a general class of manipulators are of great importance. In this paper, we use convex optimization techniques to solve the inverse kinematics problem with joint limit constraints for highly redundant serial kinematic chains with spherical joints in two and three dimensions. This is accomplished through a novel formulation of inverse kinematics as a nearest point problem, and with a fast sum of squares solver that exploits the sparsity of kinematic constraints for serial manipulators. Our method has the advantages of post-hoc certification of global optimality and a runtime that scales polynomially with the number of degrees of freedom. Additionally, we prove that our convex relaxation leads to a globally optimal solution when certain conditions are met, and demonstrate empirically that these conditions are common and represent many practical instances. Finally, we provide an open source implementation of our algorithm.

关键词： Kinematics Optimization Conferences Automation Robots Task analysis

Self-Supervised Deep Pose Corrections for Robust Visual Odometry

学校读者我要写书评

暂无评论

Self-Supervised Deep Pose Corrections for Robust Visual Odom...

IEEE International Conference on robotics and Automation (ICRA)

作者： Brandon Wagstaff Valentin Peretroukhin Jonathan Kelly Space & Terrestrial Autonomous Robotic Systems (STARS) Laboratory University of Toronto Institute for Aerospace Studies (UTIAS) Toronto Ontario Canada

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

We present a self-supervised deep pose correction (DPC) network that applies pose corrections to a visual odometry estimator to improve its accuracy. Instead of regressing inter-frame pose changes directly, we build on prior work that uses data-driven learning to regress pose corrections that account for systematic errors due to violations of modelling assumptions. Our self-supervised formulation removes any requirement for six-degrees-of-freedom ground truth and, in contrast to expectations, often improves overall navigation accuracy compared to a supervised approach. Through extensive experiments, we show that our self-supervised DPC network can significantly enhance the performance of classical monocular and stereo odometry estimators and substantially out-performs state-of-the-art learning-only approaches.

关键词： Image reconstruction Cameras Training Lighting Pipelines Robustness Visual odometry