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IEEE International Conference on robotics and Automation (ICRA)

作者： Nathan Ratliff Marc Toussaint Stefan Schaal Autonomous Motion Department Max Planck Institute for Intelligent Systems Tübingen Germany Machine Learning and Robotics Lab University of Stuttgart Germany

ISBN: (纸本)9781479969241

What is it that makes movement around obstacles hard? The answer seems clear: obstacles contort the geometry of the workspace and make it difficult to leverage what we consider easy and intuitive straight-line Cartesian geometry. But is Cartesian motion actually easy? It's certainly well-understood and has numerous applications. But beneath the details of linear algebra and pseudoinverses, lies a non-trivial Riemannian metric driving the solution. Cartesian motion is easy only because the pseudoinverse, our powerhouse tool, correctly represents how Euclidean workspace geometry pulls back into the configuration space. In light of that observation, it reasons that motion through a field of obstacles could be just as easy as long as we correctly account for how those obstacles warp the geometry of the space. This paper explores extending our geometric model of the robot beyond the notion of a Cartesian workspace space to fully model and leverage how geometry changes in the presence of obstacles. Intuitively, impenetrable obstacles form topological holes and geodesics curve around them accordingly. We formalize this intuition and develop a general motion optimization framework called Riemannian Motion Optimization (RieMO) to efficiently find motions using our geometric models. Our experiments demonstrate that, for many problems, obstacle avoidance can be much more natural when placed within the right geometric context.

关键词： Geometry Optimization Measurement Approximation methods Trajectory Robots Kinematics

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Using robotics to train students for Industry 4.0

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IFAC-PapersOnLine 2019年第9期52卷 153-158页

作者： Elisa Tosello Nicola Castaman Emanuele Menegatti Intelligent Autonomous Systems Lab (IAS-Lab) Department of Information Engineering University of Padova Via Gradenigo 6/B 35131 Padova Italy IT+Robotics srl Contrà Valmerlara 21 36100 Vicenza Italy

This paper presents the master course on autonomous robotics that we offer at the School of Engineering of the University of Padova (Italy). Its novelty is the assignment of a lab project carefully designed to train students on autonomous and industrial robotics in the framework of Industry 4.0: the “Industry 4.0 robotics Challenge”. Students have to program both a manipulator and a mobile robot, together with a 3D vision system, in order to collaborate in the fulfillment of a pick-place-transport industrial task. We adopt a constructionist approach: project-based learning and team-based learning are applied to robotics and Industry 4.0. The project is organized as a challenge to motivate students to propose innovative ideas. A survey on students’ satisfaction is reported at the end of the paper. We made the description of both the hardware and software setup, together with tutorials and wikis, publicly available to let other robotics instructors replicate our proposal and make it a point of reference for teaching robotics in the frame of Industry 4.0.

关键词： Education robotics ROS Industry 4.0 Robotic manipulation Robot navigation

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HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers

HortiBot: An Adaptive Multi-Arm System for Robotic Horticult...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Christian Lenz Rohit Menon Michael Schreiber Melvin Paul Jacob Sven Behnke Maren Bennewitz Autonomous Intelligent Systems Lab University of Bonn Germany Center for Robotics Bonn Germany Humanoid Robots Lab University of Bonn Germany The Lamarr Institute Bonn Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Horticultural tasks such as pruning and selective harvesting are labor intensive and horticultural staff are hard to find. Automating these tasks is challenging due to the semi-structured greenhouse workspaces, changing environmental conditions such as lighting, dense plant growth with many occlusions, and the need for gentle manipulation of non-rigid plant organs. In this work, we present the three-armed system HortiBot, with two arms for manipulation and a third arm as an articulated head for active perception using stereo cameras. Its perception system detects not only peppers, but also peduncles and stems in real time, and performs online data association to build a world model of pepper plants. Collision-aware online trajectory generation allows all three arms to safely track their respective targets for observation, grasping, and cutting. We integrated perception and manipulation to perform selective harvesting of peppers and evaluated the system in lab experiments. Using active perception coupled with end-effector force torque sensing for compliant manipulation, HortiBot achieves high success rates in our indoor pepper plant mock-up.

关键词： Torque Target tracking Horticulture Active perception Lighting Robot sensing systems Real-time systems Trajectory Sensors intelligent robots

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HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers

arXiv

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arXiv 2024年

作者： Lenz, Christian Menon, Rohit Schreiber, Michael Jacob, Melvin Paul Behnke, Sven Bennewitz, Maren Autonomous Intelligent Systems Lab University of Bonn Germany Humanoid Robots Lab University of Bonn Germany Center for Robotics Bonn Germany The Lamarr Institute Bonn Germany

关键词： End effectors

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Learning interaction for collaborative tasks with probabilistic movement primitives

Learning interaction for collaborative tasks with probabilis...

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IEEE-RAS International Conference on Humanoid Robots

作者： Guilherme Maeda Marco Ewerton Rudolf Lioutikov Heni Ben Amor Jan Peters Gerhard Neumann Intelligent Autonomous Systems Lab Technische Universitaet Darm-stadt Darmstadt Germany Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA USA Max Planck Institute for Intelligent Systems Tuebingen Germany

This paper proposes a probabilistic framework based on movement primitives for robots that work in collaboration with a human coworker. Since the human coworker can execute a variety of unforeseen tasks a requirement of our system is that the robot assistant must be able to adapt and learn new skills on-demand, without the need of an expert programmer. Thus, this paper leverages on the framework of imitation learning and its application to human-robot interaction using the concept of Interaction Primitives (IPs). We introduce the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and generates the appropriate movement primitive of the robot assistant. We evaluate our method on experiments using a lightweight arm interacting with a human partner and also using motion capture trajectories of two humans assembling a box. The advantages of ProMPs in relation to the original formulation for interaction are exposed and compared.

关键词： Trajectory Robot kinematics Hidden Markov models Collaboration Vectors Probabilistic logic

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Complete and scalable multi-robot planning in tunnel environments

Complete and scalable multi-robot planning in tunnel environ...

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作者： Peasgood, Mike McPhee, John Clark, Christopher Lab for Intelligent and Autonomous Robotics Department of Mechanical Engineering University of Waterloo Waterloo ON N2L 3G1 Canada Motion Research Group Department of Systems Design Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)8587978128

This paper addresses the challenging problem of finding collision- free trajectories for many robots moving to individual goals within a common environment. Most popular algorithms for multi-robot planning manage the complexity of the problem by planning trajectories for robots sequentially, such decoupled methods may fail to and a solution even if one exists. In contrast, this paper describes a multi-phase approach to the planning problem that guarantees a solution by creating and maintaining obstacle-free paths through the environment as required for each robot to reach its goal. Using a topological graph and spanning tree representation of a tunnel or corridor environment, the multi-phase planner is capable of planning trajectories for up to r = L¡1 robots, where the spanning tree includes L leaves. Monte Carlo simulations in a large environment with varying number of robots demonstrate that the algorithm can solve planning problems requiring complex coordination of many robots that cannot be solved with a decoupled approach, and is scalable with complexity linear in the number of robots.

关键词： Motion planning

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Modelling mobile robot navigation in 3D environments: Camera-based stairs recognition in Gazebo 3

Modelling mobile robot navigation in 3D environments: Camera...

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3rd Moscow Workshop on Electronic and Networking Technologies, MWENT 2022

作者： Mustafin, Maksim Tsoy, Tatyana Martinez-Garcia, Edgar A. Meshcheryakov, Roman Magid, Evgeni Kazan Federal University Intelligent Robotics Department Kazan Russia Autonomous University of Ciudad Juarez Industrial Engineering and Manufacturing Dept. Chihuahua Mexico V. A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Cyber-physical Lab Moscow Russia HSE University Moscow Russia

ISBN: (纸本)9781665496650

The task of sensory-based autonomous navigation of mobile robots requires data fusion from multiple sources in order to properly detect and recognize environmental obstacles. One of important issues mobile robots deal in a typical multi-level indoor environment is a stair well detection and negotiation. This paper presents ROS-based stairs detection implementation using onboard cameras of the Russian mobile crawler robot Servosila Engineer. Virtual experiments were performed in Gazebo environment with a single camera and a stereo camera. © 2022 IEEE.

关键词： Computer vision

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PACC: A Passive-Arm Approach for High-Payload Collaborative Carrying with Quadruped Robots Using Model Predictive Control

PACC: A Passive-Arm Approach for High-Payload Collaborative ...

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IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Giulio Turrisi Lucas Schulze Vivian S. Medeiros Claudio Semini Victor Barasuol Dynamic Legged Systems Lab Istituto Italiano di Tecnologia (IIT) Intelligent Autonomous Systems Lab TU Darmstadt Legged Robotics Group Sao Paulo University (USP)

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce the concept of using passive arm structures with intrinsic impedance for robot-robot and human-robot collaborative carrying with quadruped robots. The concept is meant for a leader-follower task and takes a minimalist approach that focuses on exploiting the robots’ payload capabilities and reducing energy consumption, without compromising the robot locomotion capabilities. We introduce a preliminary arm mechanical design and describe how to use its joint displacements to guide the robot’s motion. To control the robot’s locomotion, we propose a decentralized Model Predictive Controller that incorporates an approximation of the arm dynamics and the estimation of the external forces from the collaborative carrying. We validate the overall system experimentally by performing both robot-robot and human-robot collaborative carrying on a stair-like obstacle and on rough terrain.

关键词： Collaboration Predictive models Manipulators Stability analysis Robustness Quadrupedal robots Impedance Collision avoidance Robots Payloads

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Real-time perception meets reactive motion generation

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IEEE robotics and Automation Letters 2018年第3期3卷 1864-1871页

作者： Kappler, Daniel Meier, Franziska Issac, Jan Mainprice, Jim Cifuentes, Cristina Garcia Wuthrich, Manuel Berenz, Vincent Schaal, Stefan Ratliff, Nathan Bohg, Jeannette Autonomous Motion Department MPI for Intelligent Systems Stuttgart70569 Germany Lula Robotics Inc. SeattleWA98102 United States CLMC Lab University of Southern California Los AngelesCA90007 United States Department of Computer Science and Engineering University of Washington SeattleWA98195 United States University of Stuttgart Stuttgart70174 Germany Department of Computer Science Stanford University StanfordCA94305 United States

We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models, and hard-to-predict environment dynamics. We quantify the importance of continuous, real-time perception and its tight integration with reactive motion generation methods in dynamic manipulation scenarios. We compare three different systems that are instantiations of the most common architectures in the field: 1) a traditional sense-plan-act approach that is still widely used;2) a myopic controller that only reacts to local environment dynamics;and 3) a reactive planner that integrates feedback control and motion optimization. All architectures rely on the same components for real-time perception and reactive motion generation to allow a quantitative evaluation. We extensively evaluate the systems on a real robotic platform in four scenarios that exhibit either a challenging workspace geometry or a dynamic environment. We quantify the robustness and accuracy that is due to integrating real-time feedback at different time scales in a reactive motion generation system. We also report on the lessons learned for system building. © 2016 IEEE.

关键词： Memory architecture

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An Evaluation of Direct Image Based Visual Tracking System for autonomous Manipulation* 48

An Evaluation of Direct Image Based Visual Tracking System f...

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

作者： Islam, S. Saleh, A. Dias, J. Sunda-Meya, A. Xavier University of Louisiana Dual Degree Engineering Program Intelligent Robotics Mechatronics & Autonomous Systems Lab 1 Drexel Drive New OrleansLA70125 United States Khalifa University United Arab Emirates Coimbra3030-260 Portugal Khalifa University Khalifa University Autonomous Vehicles Lab United Arab Emirates Xavier University of Louisiana College of Arts & Sciences 1 Drexel Drive New OrleansLA70125 United States

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper, we implement and evaluate a direct image-based visual tracking system for autonomous manipulation applications. The direct image-based visual tracking method is developed to relax complex image processing tasks from traditional image and position-based visual tracking methods. The visual signals are constructed by using multiresolution coefficients. The method compares the multiresolution image transformation of the current and desired images and uses the mismatch between them to drive the system. The method needs to design a multiresolution interaction matrix with half and details images. The matrix connects the multiresolution image transformation coefficients with the velocity of the manipulator and controller. To illustrate the effectiveness, the direct image-based tracking system design is implemented and evaluated to locate the position of the valve stem and set of wrenches for autonomous grasping and manipulation for fire fighting applications. © 2022 IEEE.

关键词： Linear transformations

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