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Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

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arXiv 2018年

作者： Petković, Tomislav Puljiz, David Marković, Ivan Hein, Björn University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Kaiserstraße 12 Deutschland KarlsruheDE-76131

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual reality

Where's waldo at time t ? using spatio-temporal models for mobile robot search

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Where's waldo at time t ? using spatio-temporal models for m...

IEEE International Conference on robotics and Automation (ICRA)

作者： Tomáš Krajník Miroslav Kulich Lenka Mudrová Rares Ambrus Tom Duckett Lincoln Centre for Autonomous Systems University of Lincoln Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Intelligent Robotics Lab University of Birmingham UK KTH Royal Institute of Technology Sweden

ISBN: (纸本)9781479969241

We present a novel approach to mobile robot search for non-stationary objects in partially known environments. We formulate the search as a path planning problem in an environment where the probability of object occurrences at particular locations is a function of time. We propose to explicitly model the dynamics of the object occurrences by their frequency spectra. Using this spectral model, our path planning algorithm can construct plans that reflect the likelihoods of object locations at the time the search is performed. Three datasets collected over several months containing person and object occurrences in residential and office environments were chosen to evaluate the approach. Several types of spatio-temporal models were created for each of these datasets and the efficiency of the search method was assessed by measuring the time it took to locate a particular object. The results indicate that modeling the dynamics of object occurrences reduces the search time by 25% to 65% compared to maps that neglect these dynamics.

关键词： Search problems Mobile robots Testing Robot sensing systems Training

autonomous Forest Inventory with Legged Robots: System Design and Field Deployment

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arXiv 2024年

作者： Mattamala, Matias Chebrolu, Nived Casseau, Benoit Freißmuth, Leonard Frey, Jonas Tuna, Turcan Hutter, Marco Fallon, Maurice Oxford Robotics Institute The University of Oxford United Kingdom Robotic Systems Lab ETH Zurich Switzerland Technical University of Munich Germany Autonomous Learning Group Max Planck Institute for Intelligent Systems Germany

We present a solution for autonomous forest inventory with a legged robotic platform. Compared to their wheeled and aerial counterparts, legged platforms offer an attractive balance of endurance and low soil impact for forest applications. In this paper, we present the complete system architecture of our forest inventory solution which includes state estimation, navigation, mission planning, and real-time tree segmentation and trait estimation. We present preliminary results for three campaigns in forests in Finland and the UK and summarize the main outcomes, lessons, and challenges. Our UK experiment at the Forest of Dean with the ANYmal D legged platform, achieved an autonomous survey of a 0.96 ha plot in 20 min, identifying over 100 trees with typical DBH accuracy of 2 cm. © 2024, CC BY.

关键词： Machine design

Dynamic Human-Aware Task Planner for Human-Robot Collaboration in Industrial Scenario

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Dynamic Human-Aware Task Planner for Human-Robot Collaborati...

European Conference on Mobile Robots (ECMR)

作者： Alberto Gottardi Matteo Terreran Christoph Frommel Manfred Schoenheits Nicola Castaman Stefano Ghidoni Emanuele Menegatti Department of Information Engineering Intelligent Autonomous System Lab University of Padova Padua Italy IT+Robotics srl Vicenza Italy Deutsches Zentrum für Luft - und Raumfahrt e.V. (DLR) Augsburg Germany

The collaboration between humans and robots in industrial scenarios is one of the key challenges for Industry 4.0. In particular, industrial robots offer accuracy and efficiency, while humans have experience and the capability to manage complex situations. Combining these features can enhance the industrial process by avoiding the user manipulates heavy weights and allowing him to dedicate his efforts to tasks where flexibility, quality and experience make the difference in the final product. However, the collaboration between humans and robots raises several new problems to be addressed like safety, tasks scheduling and operator ergonomics. For example, human presence in the robot workspace introduces various elements of complexity into robot planning due to its dynamism and unpredictability. Planning must take into account how to coordinate the tasks between the robot and the human and be quick in re-planning to respond reactively to the operator's trigger. For this purpose, this work proposes a hierarchical Human-Aware Task Planner framework capable of generate a suitable plan to complete the process and manage user interrupts in order to have a constantly updated plan. The method is evaluated in a real industrial scenario and in a specific complex assembly task like the draping of carbon fiber plies.

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Inertial Estimation and Energy-Efficient Control of a Cable-suspended Load with a Team of UAVs

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Inertial Estimation and Energy-Efficient Control of a Cable-...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： A. Petitti D. Sanalitro M. Tognon A. Milella J. Cortés A. Franchi Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA) CNR Bari Italy LAAS-CNRS Université de Toulouse CNRS Toulouse France Autonomous Systems Lab ETH Zurich Zürich Switzerland Robotics and Mechatronics lab University of Twente Enschede The Netherlands

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

The Fly-Crane is a multi-robot aerial manipulator system composed of three aerial vehicles towed to a platform by means of six cables. This paper presents a method to estimate the mass and the position of the center of mass of a loaded platform (i.e. the Fly-Crane platform including a transported load). The precise knowledge of these parameters allows to sensibly minimize the total effort exerted during a full-pose manipulation task The estimation is based on the measure of the forces applied by the aerial vehicles to the platform in different static configurations. We demonstrate that only two different configurations are sufficient to estimate the inertial parameters. Far-from-ideal numerical simulations show the effectiveness of the estimation method. Once the parameters are estimated, we show the enhancement of the system performances by minimizing the total exerted effort. The validity of the proposed algorithm in non-ideal conditions is presented through simulations based on the Gazebo simulator.

关键词： Estimation Task analysis Numerical models Robots Vehicle dynamics Trajectory Load modeling

Optimized Lane Assignment Using Inter-Vehicle Communication

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Optimized Lane Assignment Using Inter-Vehicle Communication

IEEE Symposium on intelligent Vehicle

作者： Thanh-Son Dao Christopher Michael Clark Jan Paul Huissoon Lab for Autonomous and Intelligent Robotics Department of Mechanical Engineering University of Waterloo ONT Canada Computer Science Department California Polytechnic State University San Louis Obispo CA USA Department of Mechanical Engineering University of Waterloo ONT Canada

This paper presents an approach to lane assignment for highway vehicles that increases traffic throughput while ensuring they exit successfully at their destinations. Most of current traffic management systems do not consider lane organization of vehicles and only regulate traffic flows by controlling traffic signals or ramp meters. However, traffic throughput and efficient use of highways can be increased by coordinating driver behaviors intelligently. The goal of this research is to form a distributed control strategy for cars themselves to select lanes using inter-vehicle communication. Initial results are promising and demonstrate that intelligent lane selection can decrease vehicle traffic time.

关键词： Road transportation Communication system traffic control Throughput Distributed control intelligent vehicles Road vehicles Control systems intelligent transportation systems intelligent robots Mechanical engineering

An overview of perception methods for horticultural robots: From pollination to harvest

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arXiv 2018年

作者： Ahn, Ho Seok Dayoub, Feras Popović, Marija MacDonald, Bruce A. Siegwart, Roland Sa, Inkyu Department of Electrical and Computer Engineering CARES University of Auckland 368 Khyber Pass Rd Newmarket Auckland1023 New Zealand Department of Mechanical and Process Engineering Institute of Robotics and Intelligent Systems Autonomous Systems Lab. ETH Zurich Switzerland

Horticultural enterprises are becoming more sophisticated as the range of the crops they target expands. Requirements for enhanced efficiency and productivity have driven the demand for automating on-field operations. However, various problems remain yet to be solved for their reliable, safe deployment in real-world scenarios. This paper examines major research trends and current challenges in horticultural robotics. Specifically, our work focuses on sensing and perception in the three main horticultural procedures: Pollination, yield estimation, and harvesting. For each task, we expose major issues arising from the unstructured, cluttered, and rugged nature of field environments, including variable lighting conditions and difficulties in fruit-specific detection, and highlight promising contemporary studies. Based on our survey, we address new perspectives and discuss future development trends. Copyright © 2018, The Authors. All rights reserved.

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A Comparison of Imitation Learning Algorithms for Bimanual Manipulation

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arXiv 2024年

作者： Drolet, Michael Stepputtis, Simon Kailas, Siva Jain, Ajinkya Peters, Jan Schaal, Stefan Amor, Heni Ben The School of Computing and Augmented Intelligence Arizona State University Tempe United States The Robotics Institute Carnegie Mellon University Pittsburgh United States Intrinsic Mountain View United States The Intelligent Autonomous Systems Lab. TU Darmstadt Darmstadt Germany

Amidst the wide popularity of imitation learning algorithms in robotics, their properties regarding hyperparameter sensitivity, ease of training, data efficiency, and performance have not been well-studied in high-precision industry-inspired environments. In this work, we demonstrate the limitations and benefits of prominent imitation learning approaches and analyze their capabilities regarding these properties. We evaluate each algorithm on a complex bimanual manipulation task involving an over-constrained dynamics system in a setting involving multiple contacts between the manipulated object and the environment. While we find that imitation learning is well suited to solve such complex tasks, not all algorithms are equal in terms of handling environmental and hyperparameter perturbations, training requirements, performance, and ease of use. We investigate the empirical influence of these key characteristics by employing a carefully designed experimental procedure and learning environment. © 2024, CC BY.

关键词： Contrastive Learning

COMPLETE AND SCAlabLE MULTI-ROBOT PLANNING IN TUNNEL ENVIRONMENTS

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IFAC Proceedings Volumes 2006年第20期39卷 26-31页

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

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 find 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： mobile robots efficient algorithms path planning trajectory planning