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CIDGIKc: Distance-Geometric Inverse Kinematics for Continuum Robots

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IEEE robotics and Automation Letters 2023年第11期8卷 7679-7686页

作者： Zhang, Hanna Jiamei Giamou, Matthew Maric, Filip Kelly, Jonathan Burgner-Kahrs, Jessica University of Toronto Continuum Robotics Laboratory Department of Mathematical and Computational Sciences TorontoONM5S 1A1 Canada McMaster University Autonomous Robotics and Convex Optimization Laboratory Department of Computing and Software HamiltonONL8S 4L8 Canada University of Toronto Space and Terrestrial Autonomous Robotic System Laboratory Institute for Aerospace Studies TorontoONM5S 1A1 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. © 2016 IEEE.

关键词： End effectors

GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation

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arXiv 2024年

作者： Bilić, Ivan Marić, Filip Bonsignorio, Fabio Petrović, Ivan 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 and Terrestrial Autonomous Robotic Systems Laboratory Canada

In autonomous robotics, measurement of the robot’s internal state and perception of its environment, including interaction with other agents such as collaborative robots, are essential. Estimating the pose of the robot arm from a single view has the potential to replace classical eye-to-hand calibration approaches and is particularly attractive for online estimation and dynamic environments. In addition to its pose, recovering the robot configuration provides a complete spatial understanding of the observed robot that can be used to anticipate the actions of other agents in advanced robotics use cases. Furthermore, this additional redundancy enables the planning and execution of recovery protocols in case of sensor failures or external disturbances. We introduce GISR - a deep configuration and robot-to-camera pose estimation method that prioritizes execution in real-time. GISR consists of two modules: (i) a geometric initialization module that efficiently computes an approximate robot pose and configuration, and (ii) a deep iterative silhouette-based refinement module that arrives at a final solution in just a few iterations. We evaluate GISR on publicly available data and show that it outperforms existing methods of the same class in terms of both speed and accuracy, and can compete with approaches that rely on ground-truth proprioception and recover only the pose. Our code is available at https://***/iwhitey/GISR-robot. © 2024, CC BY.

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A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image

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arXiv 2023年

作者： Bilić, Ivan Marić, Filip Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Space and Terrestrial Autonomous Robotic Systems Laboratory Canada

autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot’s kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results. © 2023, CC BY-NC-ND.

关键词： Manipulators

Automated Planning Domain Inference for Task and Motion Planning

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arXiv 2024年

作者： Huang, Jinbang Tao, Allen Marco, Rozilyn Bogdanovic, Miroslav Kelly, Jonathan Shkurti, Florian Space and Terrestrial Autonomous Systems Lab Canada Robot Vision and Learning Lab University of Toronto Robotics Institute Canada

Task and motion planning (TAMP) frameworks address long and complex planning problems by integrating high-level task planners with low-level motion planners. However, existing TAMP methods rely heavily on the manual design of planning domains that specify the preconditions and postconditions of all high-level actions. This paper proposes a method to automate planning domain inference from a handful of test-time trajectory demonstrations, reducing the reliance on human design. Our approach incorporates a deep learning-based estimator that predicts the appropriate components of a domain for a new task and a search algorithm that refines this prediction, reducing the size and ensuring the utility of the inferred domain. Our method is able to generate new domains from minimal demonstrations at test time, enabling robots to handle complex tasks more efficiently. We demonstrate that our approach outperforms behavior cloning baselines, which directly imitate planner behavior, in terms of planning performance and generalization across a variety of tasks. Additionally, our method reduces computational costs and data amount requirements at test time for inferring new planning domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data

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arXiv 2023年

作者： Janda, Andrej Wagstaff, Brandon Ng, Edwin G. Kelly, Jonathan Space & Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Canada

Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is particularly important for semantic segmentation tasks involving 3D datasets, which are often significantly smaller and more challenging to annotate than their image-based counterparts. Self-supervised pre-training on unlabelled data is one way to reduce the amount of manual annotations needed. Previous work has focused on pre-training with point clouds exclusively. While useful, this approach often requires two or more registered views. In the present work, we combine image and point cloud modalities by first learning self-supervised image features and then using these features to train a 3D model. By incorporating image data, which is often included in many 3D datasets, our pre-training method only requires a single scan of a scene and can be applied to cases where localization information is unavailable. We demonstrate that our pre-training approach, despite using single scans, achieves comparable performance to other multi-scan, point cloud-only methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data

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Contrastive Learning for Self-Supervised Pre-Training of Poi...

Canadian Conference on Computer and Robot Vision, CRV

作者： Andrej Janda Brandon Wagstaff Edwin G. Ng Jonathan Kelly Space & Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Canada

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Extrinsic Calibration of 2D Millimetre-Wavelength Radar Pairs Using Ego-Velocity Estimates

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Extrinsic Calibration of 2D Millimetre-Wavelength Radar Pair...

IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Qilong Cheng Emmett Wise Jonathan Kelly Space & Terrestrial Autonomous Robotics Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada Vector Institute Faculty

Correct radar data fusion depends on knowledge of the spatial transform between sensor pairs. Current methods for determining this transform operate by aligning identifiable features in different radar scans, or by relying on measurements from another, more accurate sensor. Feature-based alignment requires the sensors to have overlapping fields of view or necessitates the construction of an environment map. Several existing techniques require bespoke retroreflective radar targets. These requirements limit both where and how calibration can be performed. In this paper, we take a different approach: instead of attempting to track targets or features, we rely on ego-velocity estimates from each radar to perform calibration. Our method enables calibration of a subset of the transform parameters, including the yaw and the axis of translation between the radar pair, without the need for a shared field of view or for specialized targets. In general, the yaw and the axis of translation are the most important parameters for data fusion, the most likely to vary over time, and the most difficult to calibrate manually. We formulate calibration as a batch optimization problem, show that the radar-radar system is identifiable, and specify the platform excitation requirements. Through simulation studies and real-world experiments, we establish that our method is more reliable and accurate than state-of-the-art methods. Finally, we demonstrate that the full rigid body transform can be recovered if relatively coarse information about the platform rotation rate is available.

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Extrinsic Calibration of 2D Millimetre-Wavelength Radar Pairs Using Ego-Velocity Estimates

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arXiv 2023年

作者： Cheng, Qilong Wise, Emmett Kelly, Jonathan The Space & Terrestrial Autonomous Robotics Systems Laboratory The University of Toronto Institute for Aerospace Studies Toronto Canada Vector Institute Faculty Affiliate Canada

关键词： Calibration

Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation

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arXiv 2023年

作者： Janda, Andrej Merriaux, Pierre Olivier, Pierre Kelly, Jonathan Space & Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Toronto Canada LeddarTech Inc. Québec Canada

Advances in lidar technology have made the collection of 3D point clouds fast and easy. While most lidar sensors return per-point intensity (or reflectance) values along with range measurements, flash lidar sensors are able to provide information about the shape of the return pulse. The shape of the return waveform is affected by many factors, including the distance that the light pulse travels and the angle of incidence with a surface. Importantly, the shape of the return waveform also depends on the material properties of the reflecting surface. In this paper, we investigate whether the material type or class can be determined from the full-waveform response. First, as a proof of concept, we demonstrate that the extra information about material class, if known accurately, can improve performance on scene understanding tasks such as semantic segmentation. Next, we learn two different full-waveform material classifiers: a random forest classifier and a temporal convolutional neural network (TCN) classifier. We find that, in some cases, material types can be distinguished, and that the TCN generally performs better across a wider range of materials. However, factors such as angle of incidence, material colour, and material similarity may hinder overall performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation