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2,639 results for “Robotic”
Legibility of Multi-Robot Systems: examples of coordination objectives
<p>Video examples used to introduce the coordination objectives to the users, when performing user studies to assess legibility of multi-robot systems.</p>
Free Lunch in Evolutionary Embodied Computation in Modular Robotics
<p>We demonstrate, based on anecdotal experimental results, that physical constraints (e.g., in physics-based simulations of evolutionary robotics) can significantly increase the diversity of results obtained by evolutionary computation methods.</p>
Development of Robotics & Automation Roadmap for Road Construction/Maintenance Projects
<p>Over the past few decades, there have been no significant improvements in the construction industry's productivity. Outside the construction industry, robots have proven to be beneficial in improving productivity, safety, and quality. There is a growing interest in adopting robots in the construction industry to address these issues. As robots get introduced to the project sites, human-robot interaction becomes an important consideration for wide-scale adoption. The success of deploying robots in the construction industry depends on how well humans collaborate with the robots. This project focused on investigating the perception of various construction industry stakeholders in adopting robots in their projects, and the level of control they feel can be delegated to robots. This project also investigated major factors that impact the decision to use a robot for a particular task. The research used a survey to collect relevant data from various construction industry stakeholders for 11 specific types of robots that were applicable to the construction industry. The main findings from this study are that there was a generally positive perception of different types of robots regarding their job relevance, ease of use, output quality, and usefulness. Furthermore, from a human-robot collaboration perspective, majority of respondents supported shared autonomy between human and robot in different scenarios. This highlights the need for developing more robots that share control with the human when executing construction tasks.</p>
Background-Foreground-Segmentation Labels for "Self-improving Semantic Perception on a Construction Robot"
<p>Background-foreground-segmentation labels the paper "Self-improving Semantic Perception on a Construction Robot" of CoRL 2021</p>
CARRT - Motion Capture Data for Robotic Human Upper Body Model
<p>As advancements in the study of human activity have progressed in recent years, researchers have directed their attention towards analyzing human daily activities to investigate a diverse range of performance metrics unconsciously optimized by individuals while engaged in specific tasks. To replicate these movements in robotic systems based on human models, researchers have developed a framework for robot motion planning capable of utilizing various optimization methods to reproduce such motions through human demonstrations. In this process, capturing the movements of the human body and the objects involved in the demonstrations is imperative, as they provide essential information for the motion planning procedure. The objective of this dataset is to present human motion data while performing activities of daily living. This dataset encompasses comprehensive and precise whole-body motion data of individuals collected using a Vicon motion capture system, which facilitated the development of a full-body model integrated into OpenSim and MATLAB. The dataset comprises nine different daily living activities and eight Range of Motion activities performed by ten healthy participants. A publicly accessible whole-body human motion database has been established, encompassing raw motion data in .c3d format, motion data in .csv format for the OpenSim model, and post-processed motion data for the MATLAB model.</p>
Motion Planning Integration on ABB Robot Manipulator for Robot Welding
<p>Integration of Motion Planning based on Moveit Robot Operating System (ROS) on ABB Robot Manipulator for Robot Welding. Stochastic Trajectory Optimization for Motion Planning (STOMP) and Rapidly exploring Random Tree Star (RRT*) are used in this implementation. STOMP is used as primary path planner as it has less computation time whereas RRT* is used as failsafe if STOMP fails to compute a plan.</p>
Path Planning for Mobile Robot
<p>Implementation of Rapid exploring Random Tree Star (RRT*) Optimization based on Ellipsoid Equation. The simulation shows two different scenario of obstacles to test the implementation. The algorithm is running on MATLAB. The communication between Gazebo simulation and MATLAB is done through Robot Operating System (ROS) framework.</p>
Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy
Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (e.g., a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. In this paper, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we use blockchain technology, originally developed for the digital currency Bitcoin. The robots are given crypto tokens that allow them to participate in the swarm's security-critical activities. The token economy is regulated via a smart contract that decides how to distribute crypto tokens among the robots depending on their contributions. We design the smart contract so that Byzantine robots soon run out of crypto tokens and can therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrate that our smart contract approach indeed works: the robots can maintain blockchain networks and a blockchain-based token economy can be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we study the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics.
Experimental Validation video for paper "Safe Multimodal Communication in Human-Robot Collaboration"
<p>Experimental Validatio video for paper Safe Multimodal Communication in Human-Robot Collaboration.</p> <p>The video shows a collaborative manipulator executing a pick-and-place task communication with the user via a multimodal fusion architecture that fuses gesture and voice. The experiment compares a safe and an unsafe interaction and highlight the differences.</p>
[DATASET 9] - PLANT-ROBOT INTERFACES FOR ENERGY HARVESTING
<p>In the framework of GrowBot project, task 7.2 aims at developing bio-hybrid energy harvesting systems based on the triboelectric effect.<br> The energy conversion occurs at plant leaves level during mechanical stimulation (i.e., wind, rain, etc.).<br> Two main components have been developed in GrowBot:<br> - Flexible artificial “leaves” (flexible electrodes covered with tailored materials) to enhance mechanical impacts with the plant leaves and further enhance power output.<br> - Minimal-invasive electrodes that establish electrical contact between GrowBots and real plants.</p> <p>DS9 aims at collecting all the experimental data gathered during these activities.</p>
[DATASET 7] - SOFT "SEARCHER-LIKE" ROBOT
<p>In the framework of GrowBot project, Task 5.4 aims at developing a robotic searcher with sensing and actuation abilities.<br> IIT has developed a modular continuum soft arm taking inspiration from the structural features of climbing plants investigated in WP3. The searcher module can explore the environment via circumnutation movements and tactile feedback.</p> <p>DS7 aims at collecting all the data related to the design and development of the soft searcher robot.</p>
Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits
<p>Most soft robots are pneumatically actuated and fabricated by molding and assembling processes that typically require many manual operations and limit complexity. Furthermore, complex control components (for example, electronic pumps and microcontrollers) must be added to achieve even simple functions. Desktop fused filament fabrication (FFF) three-dimensional printing provides an accessible alternative with less manual work and the capability of generating more complex structures. However, because of material and process limitations, FFF-printed soft robots often have a high effective stiffness and contain a large number of leaks, limiting their applications. We present an approach for the design and fabrication of soft, airtight pneumatic robotic devices using FFF to simultaneously print actuators with embedded fluidic control components. We demonstrated this approach by printing actuators an order of magnitude softer than those previously fabricated using FFF and capable of bending to form a complete circle. Similarly, we printed pneumatic valves that control a high-pressure airflow with low control pressure. Combining the actuators and valves, we demonstrated a monolithically printed electronics-free autonomous gripper. When connected to a constant supply of air pressure, the gripper autonomously detected and gripped an object and released the object when it detected a force due to the weight of the object acting perpendicular to the gripper. The entire fabrication process of the gripper required no posttreatment, postassembly, or repair of manufacturing defects, making this approach highly repeatable and accessible. Our proposed approach represents a step toward complex, customized robotic systems and components created at distributed fabricating facilities.</p>
Dataset for the paper "How to Work on Equality and Inclusion when Introducing Computational Thinking and Educational Robotics in Early Childhood Education: A Systematic Review"
<p>Resources for the Systematic Literature Review (SLR) about Computational Thinking and Educational Robotics in Early Childhood Education for fostering equality and inclusion. The SLR is related to the project "COEDUIN-Alfabetización digital y STEAM en edades tempranas: propuesta co-educativa inclusiva" funded by Fundación Caja Canarias and Fundación La Caixa (ref. 2020EDU08).</p> <p>The SLR covers papers in WoS and Scopus from 2011 to 2022.</p>
Multi-contact loco-manipulation trajectories for the ANYmal robot with a 6-DoF Arm
<p>Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed based on a system's ability to coordinate complex holistic movements and multiple contact interactions when solving different tasks. However, existing approaches have been merely able to shape such behaviors with hand-crafted state machines, densely engineered rewards, or pre-recorded expert demonstrations. Here, we propose a minimally-guided framework that automatically discovers whole-body trajectories jointly with contact schedules for solving general loco-manipulation tasks in pre-modeled environments. The key insight is that multi-modal problems of this nature can be modeled within the context of integrated Task and Motion Planning (TAMP), resulting in a tractable bilevel optimization formulation. An effective bilevel search strategy is achieved owing to the fusion of domain-specific rules with the well-established strengths of different planning techniques: trajectory optimization and informed graph search, coupled with sampling-based planning. We showcase emergent behaviors for a quadrupedal mobile manipulator exploiting both prehensile and non-prehensile interactions to perform real-world tasks such as opening/closing heavy dishwashers and traversing spring-loaded doors. These behaviors are also deployed on the real system using a two-layer whole-body tracking controller.</p>
Video of Automated path planning for 3D robotic filament winding of high-performance composite materials
<p>Video showing the results of the reasearch article "Automated path planning for 3D robotic filament<br> winding of high-performance composite materials"</p>
Bags Recorded with MowIt AgRob Modular E Robot in vineyard at Terras Gauda
<p>Bags Recorded @Terra Gauda Vineyard in Spain (41.93816100127097, -8.791314972313891) with all the sensors available in the robot:</p> <p> </p> <ol> <li>Topic: /teleop/cmd_vel | Type: geometry_msgs/msg/Twist</li> <li>Topic: /robot_electrical_data | Type: itrci_hardware/msg/RobotElectricalData</li> <li>Topic: /safety_system_flag | Type: std_msgs/msg/Int16</li> <li>Topic: /clock | Type: rosgraph_msgs/msg/Clock</li> <li>Topic: /map_server/transition_event | Type: lifecycle_msgs/msg/TransitionEvent</li> <li>Topic: /navigation_current_state | Type: std_msgs/msg/String</li> <li>Topic: /spraying/cmd_vel | Type: geometry_msgs/msg/Twist</li> <li>Topic: /joy | Type: sensor_msgs/msg/Joy</li> <li>Topic: /rover/navclock | Type: ublox_msgs/msg/NavCLOCK</li> <li>Topic: /laser_security_area | Type: visualization_msgs/msg/Marker</li> <li>Topic: /debug/TimeLoadNSec | Type: std_msgs/msg/Float64</li> <li>Topic: /PathWithEndOffset | Type: itrci_nav/msg/ParametricPathSetWithEndOffset</li> <li>Topic: /Detections | Type: vision_msgs/msg/Detection2DArray</li> <li>Topic: /rover/navsvin | Type: ublox_msgs/msg/NavSVIN</li> <li>Topic: /ActualPath | Type: itrci_nav/msg/ParametricPathSet</li> <li>Topic: /rover/navstatus | Type: ublox_msgs/msg/NavSTATUS</li> <li>Topic: /splines/cmd_vel | Type: geometry_msgs/msg/Twist</li> <li>Topic: /base/navpvt | Type: ublox_msgs/msg/NavPVT</li> <li>Topic: /pose_goal | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /base/navsvin | Type: ublox_msgs/msg/NavSVIN</li> <li>Topic: /imu/data_raw | Type: sensor_msgs/msg/Imu</li> <li>Topic: /orientation_error | Type: std_msgs/msg/Float64</li> <li>Topic: /Path | Type: itrci_nav/msg/ParametricPathSet</li> <li>Topic: /base/aidalm | Type: ublox_msgs/msg/AidALM</li> <li>Topic: /imu/rpy | Type: geometry_msgs/msg/Vector3Stamped</li> <li>Topic: /FrontLaserScanObstacles | Type: sensor_msgs/msg/LaserScan</li> <li>Topic: /rover/aideph | Type: ublox_msgs/msg/AidEPH</li> <li>Topic: /base/diagnostics | Type: diagnostic_msgs/msg/DiagnosticArray</li> <li>Topic: /BackLaserScanObstacles | Type: sensor_msgs/msg/LaserScan</li> <li>Topic: /base/aideph | Type: ublox_msgs/msg/AidEPH</li> <li>Topic: /LaserScanObstacles | Type: sensor_msgs/msg/LaserScan</li> <li>Topic: /localization_stars_link/gnss_pose | Type: geometry_msgs/msg/PoseWithCovarianceStamped</li> <li>Topic: /cmd_vel | Type: geometry_msgs/msg/Twist</li> <li>Topic: /parametric_trajectories_control_node/Patch | Type: visualization_msgs/msg/MarkerArray</li> <li>Topic: /path_percent_done | Type: std_msgs/msg/Float64</li> <li>Topic: /rslidar_scan | Type: sensor_msgs/msg/LaserScan</li> <li>Topic: /odom | Type: nav_msgs/msg/Odometry</li> <li>Topic: /goal_pose | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /path_it | Type: std_msgs/msg/Float64</li> <li>Topic: /sprayer_prescription | Type: std_msgs/msg/String</li> <li>Topic: /control_status | Type: itrci_nav/msg/ParametricTrajectoriesControlStatus</li> <li>Topic: /rslidar_points | Type: sensor_msgs/msg/PointCloud2</li> <li>Topic: /base/rxmrawx | Type: ublox_msgs/msg/RxmRAWX</li> <li>Topic: /TrajectoryControlStatus | Type: std_msgs/msg/String</li> <li>Topic: /PathNoStackUp | Type: itrci_nav/msg/ParametricPathSet</li> <li>Topic: /localization_stars_link/fix_pos | Type: geometry_msgs/msg/PointStamped</li> <li>Topic: /rover/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9</li> <li>Topic: /parametric_trajectories_editor_node/update | Type: visualization_msgs/msg/InteractiveMarkerUpdate</li> <li>Topic: /base/navstatus | Type: ublox_msgs/msg/NavSTATUS</li> <li>Topic: /parameter_events | Type: rcl_interfaces/msg/ParameterEvent</li> <li>Topic: /PathNoStackUp2 | Type: itrci_nav/msg/ParametricPathSet2</li> <li>Topic: /tf_static | Type: tf2_msgs/msg/TFMessage</li> <li>Topic: /motion_current_state | Type: std_msgs/msg/String</li> <li>Topic: /base/fix | Type: sensor_msgs/msg/NavSatFix</li> <li>Topic: /TrajectoryControlCommand | Type: std_msgs/msg/String</li> <li>Topic: /clicked_point | Type: geometry_msgs/msg/PointStamped</li> <li>Topic: /motors_speed_ref | Type: itrci_hardware/msg/MotorsArrayInput</li> <li>Topic: /base/navstate | Type: ublox_msgs/msg/NavSAT</li> <li>Topic: /base/monhw | Type: ublox_msgs/msg/MonHW</li> <li>Topic: /rosout | Type: rcl_interfaces/msg/Log</li> <li>Topic: /base/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9</li> <li>Topic: /ControlPathDataFeedForward | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /localization_stars_link/nav_pos_rel | Type: geometry_msgs/msg/PointStamped</li> <li>Topic: /battery_current | Type: std_msgs/msg/Int32</li> <li>Topic: /localization_stars_link/gnss_heading | Type: visualization_msgs/msg/Marker</li> <li>Topic: /rover/fix | Type: sensor_msgs/msg/NavSatFix</li> <li>Topic: /initialpose | Type: geometry_msgs/msg/PoseWithCovarianceStamped</li> <li>Topic: /debug/TimeCycleSec | Type: std_msgs/msg/Float64</li> <li>Topic: /localization_stars_link/magnetometer_heading | Type: visualization_msgs/msg/Marker</li> <li>Topic: /map/prescription | Type: nav_msgs/msg/OccupancyGrid</li> <li>Topic: /localization_stars_link/magnetometer_heading_array | Type: visualization_msgs/msg/MarkerArray</li> <li>Topic: /joy/set_feedback | Type: sensor_msgs/msg/JoyFeedback</li> <li>Topic: /position_goal | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /motors_outputs | Type: itrci_hardware/msg/MotorsArrayOutput</li> <li>Topic: /mowit_scissors_motor_driver_node/duty_cycle_ref | Type: std_msgs/msg/Int32</li> <li>Topic: /localization_stars_link/gnss_heading_array | Type: visualization_msgs/msg/MarkerArray</li> <li>Topic: /rover/rxmrawx | Type: ublox_msgs/msg/RxmRAWX</li> <li>Topic: /base/fix_velocity | Type: geometry_msgs/msg/TwistWithCovarianceStamped</li> <li>Topic: /rover/navstate | Type: ublox_msgs/msg/NavSAT</li> <li>Topic: /parametric_trajectories_editor_node/feedback | Type: visualization_msgs/msg/InteractiveMarkerFeedback</li> <li>Topic: /tf | Type: tf2_msgs/msg/TFMessage</li> <li>Topic: /parametric_trajectories_control_node/Point1 | Type: visualization_msgs/msg/Marker</li> <li>Topic: /base/rxmrtcm | Type: ublox_msgs/msg/RxmRTCM</li> <li>Topic: /parametric_trajectories_control_node/Point2 | Type: visualization_msgs/msg/Marker</li> <li>Topic: /rover/fix_velocity | Type: geometry_msgs/msg/TwistWithCovarianceStamped</li> <li>Topic: /rover/rxmrtcm | Type: ublox_msgs/msg/RxmRTCM</li> <li>Topic: /ControlPathData | Type: geometry_msgs/msg/PoseStamped</li> <li>Topic: /base/navclock | Type: ublox_msgs/msg/NavCLOCK</li> <li>Topic: /battery_voltage | Type: std_msgs/msg/Int32</li> <li>Topic: /safety_current_state | Type: std_msgs/msg/String</li> <li>Topic: /raw_can | Type: std_msgs/msg/String</li> <li>Topic: /rover/navpvt | Type: ublox_msgs/msg/NavPVT</li> <li>Topic: /rover/monhw | Type: ublox_msgs/msg/MonHW</li> <li>Topic: /path_num | Type: std_msgs/msg/Float64</li> <li>Topic: /rover/aidalm | Type: ublox_msgs/msg/AidALM</li> <li>Topic: /rover/navheading | Type: sensor_msgs/msg/Imu</li> <li>Topic: /position_error | Type: std_msgs/msg/Float64</li> <li>Topic: /rover/diagnostics | Type: diagnostic_msgs/msg/DiagnosticArray</li> <li>Topic: /base/navheading | Type: sensor_msgs/msg/Imu</li> </ol> <p><br> <br> </p>
Robot Cleaner. Made in USSR
A fantasy on the theme of retrofuturism about what Soviet people would think if they heard the phrase "Robot vacuum cleaner". The prototype of the head is a Soviet robot guide at the Polytechnic Museum named Sepulka. Source: Objaverse 1.0 / Sketchfab
Dataset AEROARMS - Perception for robot operation
<p><strong>Dataset description: </strong>This dataset contains the images used for validating the perception algorithms to support aerial and ground robot operations.</p> <p><strong>Data description:</strong> The images where obtained using an Intel Realsense d435 camera. Additionally, the calibration of the camera is given in XML format.</p> <p><strong>Dataset size: </strong>72.8Mb</p>
Cardiovascular Rehabilitation Early After Stroke Using Feedback-controlled Robotics-assisted Treadmill Exercise
ClinicalTrials.gov study NCT01679600. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Effects of Overground Robot-Assisted Gait Training on Stroke-Related Sarcopenia
ClinicalTrials.gov study NCT06910254. IPD Sharing: NO. Countries: 1. Publications: 14.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.