Inverse Kinematics with Forward Dynamics Solvers for Sampled Motion Tracking
FZI Research Center for Information Technology（FZI信息技术研究中心）；IAR Institute for Anthropomatics and Robotics（IAR拟人系统与机器人研究所）；KIT Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Stefan Scherzinger, Arne Roennau, Rüdiger Dillmann
备注：
7 pages, 8 figures, presented at the 19th International Conference on Advanced Robotics (ICAR) 2019, Belo Horizonte, Brazil
Tracking Cartesian motion with end~effectors is a fundamental task in robot control. For motion that is not known in advance, the solvers must find fast solutions to the inverse kinematics (IK) problem for discretely sampled target poses. On joint control level, however, the robot's actuators operate in a continuous domain, requiring smooth transitions between individual states. In this work, we present a boost to the well-known Jacobian transpose method to address this goal, using the mass matrix of a virtually conditioned twin of the manipulator. Results on the UR10 show superior convergence and quality of our dynamics-based solver against the plain Jacobian method. Our algorithm is straightforward to implement as a controller, using common robotics libraries.
This paper describes a new approach allowing the generation of a simplified Biped gait. This approach combines a classical dynamic modeling with an inverse kinematics' solver based on particle swarm optimization, PSO. First, an inverted pendulum, IP, is used to obtain a simplified dynamic model of the robot and to compute the target position of a key point in biped locomotion, the Centre Of Mass, COM. The proposed algorithm, called IK-PSO, Inverse Kinematics PSO, returns and inverse kinematics solution corresponding to that COM respecting the joints constraints. In This paper the inertia weigh…
This paper proposes a novel inverse kinematics (IK) solver of articulated robotic systems for path planning. IK is a traditional but essential problem for robot manipulation. Recently, data-driven methods have been proposed to quickly solve the IK for path planning. These methods can handle a large amount of IK requests at once with the advantage of GPUs. However, the accuracy is still low, and the model requires considerable time for training. Therefore, we propose an IK solver that improves accuracy and memory efficiency by utilizing the continuous hidden dynamics of Neural ODE. The performa…
A well-known weakness of the probabilistic path planners is the so-called narrow passage problem, where a region with a relatively low probability of being sampled must be explored to find a solution path. Many strategies have been proposed to alleviate this problem, most of them based on biasing the sampling distribution. When kinematic constraints appear in the problem, the configuration space typically becomes a non-parametrizable, implicit manifold. Unfortunately, this invalidates most of the existing sampling bias approaches, which rely on an explicit parametrization of the space to explo…
Mirror-Descent Inverse Kinematics for Box-constrained Joint Space
National Institute of Informatics (NII)（国立信息学研究所）；The Graduate University for Advanced Studies (SOKENDAI)（综合研究大学院大学（SOKENDAI））；Nara Institute of Science and Technology（奈良科学技术研究所）
We present an analytical solution for the inverse kinematics (IK) of a robotic arm with one prismatic joint and four revolute joints. This 5-DoF design is a result of minimizing weight while preserving functionality of the device in a wearable usage context. Generally, the IK problem for a 5-DoF robot does not guarantee solutions due to the system being over-constrained. We obtain an analytical solution by applying geometric projections and limiting the ranges of motion for each DoF. We validate this solution by reconstructing randomly sampled end-effector poses, and find position errors below…
Duke University（杜克大学）；Third Wave Automation（第三波自动化公司）；Brown University（布朗大学）
Inverse kinematics - finding joint poses that reach a given Cartesian-space end-effector pose - is a common operation in robotics, since goals and waypoints are typically defined in Cartesian space, but robots must be controlled in joint space. However, existing inverse kinematics solvers return a single solution pose, where systems with more than 6 degrees of freedom support infinitely many such solutions, which can be useful in the presence of constraints, pose preferences, or obstacles. We introduce a method that uses a deep neural network to learn to generate a diverse set of samples from …
Forward and backward reaching inverse kinematics (FABRIK) is a heuristic inverse kinematics solver that is gradually applied to manipulators with the advantages of fast convergence and generating more realistic configurations. However, under the high error constraint, FABRIK exhibits unstable convergence behavior, which is unsatisfactory for the real-time motion planning of manipulators. In this paper, a novel inverse kinematics algorithm that combines FABRIK and the sequential quadratic programming (SQP) algorithm is presented, in which the joint angles deduced by FABRIK will be taken as the …
M.S. in Data Science, Clarkson University, USA（数据科学硕士，克兰顿大学，美国）；Mathematics, Clarkson University, USA（数学，克兰顿大学，美国）
Conventional robots possess a limited understanding of their kinematics and are confined to preprogrammed tasks, hindering their ability to leverage tools efficiently. Driven by the essential components of tool usage - grasping the desired outcome, selecting the most suitable tool, determining optimal tool orientation, and executing precise manipulations - we introduce a pioneering framework. Our novel approach expands the capabilities of the robot's inverse kinematics solver, empowering it to acquire a sequential repertoire of actions using tools of varying lengths. By integrating a simulatio…
Serial robot arms have complicated kinematic equations which must be solved to write effective arm planning and control software (the Inverse Kinematics Problem). Existing software packages for inverse kinematics often rely on numerical methods which have significant shortcomings. Here we report a new symbolic inverse kinematics solver which overcomes the limitations of numerical methods, and the shortcomings of previous symbolic software packages. We integrate Behavior Trees, an execution planning framework previously used for controlling intelligent robot behavior, to organize the equation s…
Riemannian Optimization for Distance-Geometric Inverse Kinematics
University of Toronto, Institute for Aerospace Studies（多伦多大学航空航天研究所）；University of Zagreb, Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）
Path planning for multiple robots is well studied in the AI and robotics communities. For a given discretized environment, robots need to find collision-free paths to a set of specified goal locations. Robots can be fully anonymous, non-anonymous, or organized in groups. Although powerful solvers for this abstract problem exist, they make simplifying assumptions by ignoring kinematic constraints, making it difficult to use the resulting plans on actual robots. In this paper, we present a solution which takes kinematic constraints, such as maximum velocities, into account, while guaranteeing a …
Collision Detection with Analytical Derivatives of Contact Kinematics
Department of Mechanical and Nuclear Engineering, Khalifa University of Science and Technology（卡利法大学科学与技术学院机械与核工程系）；Khalifa University Center for Autonomous Robotic Systems (KUCARS)（卡利法大学自主机器人系统中心）；The BioRobotics Institute, Scuola Superiore Sant’Anna（圣安娜高等学院生物机器人研究所）；Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna（圣安娜高等学院机器人与人工智能卓越部门）；LS2N Laboratory, Institut Mines Telecom Atlantique（Atlantique电信学院LS2N实验室）
Sampling-based motion planners such as RRT* and BIT*, when applied to kinodynamic motion planning, rely on steering functions to generate time-optimal solutions connecting sampled states. Implementing exact steering functions requires either analytical solutions to the time-optimal control problem, or nonlinear programming (NLP) solvers to solve the boundary value problem given the system's kinodynamic equations. Unfortunately, analytical solutions are unavailable for many real-world domains, and NLP solvers are prohibitively computationally expensive, hence fast and optimal kinodynamic motion…
Multimodal Control of Manipulators: Coupling Kinematics and Vision for Self-Driving Laboratory Operations
1 Department of Electronics Systems, Aalborg University, Denmark；3 Extreme Robotics Laboratory, School of Metallurgy \& Materials, University of Birmingham, Birmingham, United Kingdom
University of Toronto（多伦多大学）；University of Zagreb（萨格勒布大学）；Vector Institute（向量研究所）
Inverse kinematics is a fundamental problem 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 be easily obtained. Therefore, computationally efficient numerical methods that can be adapted to a general class of manipulators are of great importance. % to motion planning and workspace generation tasks. In this paper, we use convex optimization techniques to…
Inverse kinematic (IK) methods recover the parameters of the joints, given the desired position of selected elements in the kinematic chain. While the problem is well-defined and low-dimensional, it has to be solved rapidly, accounting for multiple possible solutions. In this work, we propose a neural IK method that employs the hierarchical structure of the problem to sequentially sample valid joint angles conditioned on the desired position and on the preceding joints along the chain. In our solution, a hypernetwork $f$ recovers the parameters of multiple primary networks {$g_1,g_2,\dots,g_N$…
University of Bremen（不来梅大学）；German Research Center for Artificial Intelligence (DFKI GmbH)（德国人工智能研究中心）
Several publications are concerned with learning inverse kinematics, however, their evaluation is often limited and none of the proposed methods is of practical relevance for rigid-body kinematics with a known forward model. We argue that for rigid-body kinematics one of the first proposed machine learning (ML) solutions to inverse kinematics -- distal teaching (DT) -- is actually good enough when combined with differentiable programming libraries and we provide an extensive evaluation and comparison to analytical and numerical solutions. In particular, we analyze solve rate, accuracy, sample …
Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Federal Research Center “Computer Science and Control” RAS（俄罗斯科学院“计算机科学与控制”联邦研究中心）
Multi-Agent Path Finding (MAPF) is a long-standing problem in Robotics and Artificial Intelligence in which one needs to find a set of collision-free paths for a group of mobile agents (robots) operating in the shared workspace. Due to its importance, the problem is well-studied and multiple optimal and approximate algorithms are known. However, many of them abstract away from the kinematic constraints and assume that the agents can accelerate/decelerate instantaneously. This complicates the application of the algorithms on the real robots. In this paper, we present a method that mitigates thi…
University of Toronto（多伦多大学）；Uber Advanced Technologies Group（优步先进技术集团）；Massachusetts Institute of Technology（麻省理工学院）；University of California San Diego（加利福尼亚大学圣迭戈分校）
We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn an iterative update model. Specifically, at each iteration, the neural network takes the feedback as input and outputs an update on the current estimation. Our approach does not have any restrictions on the forward process; it does not require any prior knowledge either. Through the feedback information, our model not only can produce accurate estimations that are coherent to the input observation but also is capabl…
MineRobot: An Actuator-Centered Kinematic Modeling and Solving Framework for Underground Mining Robots
College of Computer Science and Engineering, Shandong University of Science and Technology（山东科技大学计算机科学与工程学院）；Shandong Lionking Software Co., Ltd.（山东狮王软件有限公司）；College of Intelligent Equipment, Shandong University of Science and Technology（山东科技大学智能装备学院）
Real-Time Conformal-Seeded Hybrid Inverse Kinematics for Offset Redundant Manipulators
VinRobotics；Nanyang Technological University（南洋理工大学）；VinRobotics JSC（VinRobotics股份公司）；Center for AI Research (CAIR), VinUniversity（VinUniversity人工智能研究中心）
Imitation learning is a way to teach robots skills that are demonstrated by humans. Transfering skills between these different kinematic structures seems to be straightforward in Cartesian space. Because of the correspondence problem, however, the result will most likely not be identical. This is why refinement is required, for example, by policy search. Policy search in Cartesian space is prone to reachability problems when using conventional inverse kinematic solvers. We propose a configurable approximate inverse kinematic solver and show that it can accelerate the refinement process conside…
Multi-Agent Path Finding has been widely studied in the past few years due to its broad application in the field of robotics and AI. However, previous solvers rely on several simplifying assumptions. They limit their applicability in numerous real-world domains that adopt nonholonomic car-like agents rather than holonomic ones. In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. For the first time, we propose a novel hierarchical search-based solver called Car-like Conflict-Based Search to address this problem. It applies a bod…
University of Padova（帕多瓦大学）；ETH Zurich（苏黎世联邦理工学院）；University of Twente（特文特大学）；LAAS-CNRS；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）
In this work we consider the problem of mobile robots that need to manipulate/transport an object via cables or robotic arms. We consider the scenario where the number of manipulating robots is redundant, i.e. a desired object configuration can be obtained by different configurations of the robots. The objective of this work is to show that communication can be used to implement cooperative local feedback controllers in the robots to improve disturbance rejection and reduce structural stress in the object. In particular we consider the realistic scenario where measurements are sampled and tran…
Achieving human-like motion in robots has been a fundamental goal in many areas of robotics research. Inverse kinematic (IK) solvers have been explored as a solution to provide kinematic structures with anthropomorphic movements. In particular, numeric solvers based on geometry, such as FABRIK, have shown potential for producing human-like motion at a low computational cost. Nevertheless, these methods have shown limitations when solving for robot kinematic constraints. This work proposes a framework inspired by FABRIK for human pose imitation in real-time. The goal is to mitigate the problems…
