Robot Self-Calibration Using Multiple Kinematic Chains—A Simulation Study on the iCub Humanoid Robot

Štěpánová, Karla; Pajdla, Tomáš; Hoffmann, Matej

doi:10.1109/lra.2019.2898320

Cited by 25 publications

(15 citation statements)

References 30 publications

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“…Incorporating the new set of DH parameters r is straightforward and works without any changes or additional costs as they replace the old DH parameters. The same holds for masses m and compliances k. However, to determine the elastic effects, one must find the static equilibrium between acting torques and elasticities described in (8). While it might be feasible for calibration (offline procedure) to use the iterative algorithm (10), it is more prohibitive for compensation (online).…”

Section: Compensationmentioning

confidence: 99%

Calibration of an Elastic Humanoid Upper Body and Efficient Compensation for Motion Planning

Tenhumberg

Bäuml

2021

2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)

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High absolute accuracy is an essential prerequisite for a humanoid robot to autonomously and robustly perform manipulation tasks while avoiding obstacles. We present for the first time a kinematic model for a humanoid upper body incorporating joint and transversal elasticities. These elasticities lead to significant deformations due to the robot's own weight, and the resulting model is implicitly defined via a torque equilibrium. We successfully calibrate this model for DLR's humanoid Agile Justin, including all Denavit-Hartenberg parameters and elasticities. The calibration is formulated as a combined least-squares problem with priors and based on measurements of the end effector positions of both arms via an external tracking system. The absolute position error is massively reduced from 21 mm to 3.1 mm on average in the whole workspace. Using this complex and implicit kinematic model in motion planning is challenging. We show that for optimization-based path planning, integrating the iterative solution of the implicit model into the optimization loop leads to an elegant and highly efficient solution. For mildly elastic robots like Agile Justin, there is no performance impact, and even for a simulated highly flexible robot with 20 times higher elasticities, the runtime increases by only 30%.

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Section: Compensationmentioning

confidence: 99%

Calibration of an Elastic Humanoid Upper Body and Efficient Compensation for Motion Planning

Tenhumberg

Bäuml

2021

2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)

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“…Traditionally, kinematic calibration of artificial skin is a procedure that is manually performed by the robot operator, and only recent work has started to automate it. [25], [26] presented a method where the iCub humanoid robot [7] performed "self-touch" actions on its robotic skin, allowing for an observation of the 3D position of the end-effector and computation of its Denavit-Hartenberg [DH] parameters [27]. While this approach is autonomous and does not rely on external measurements, the iCub's bi-manual self-touch capabilities would not be possible on commercially-available collaborative manipulators due a lower number of Degrees of Freedom (DoFs) and consequently reduced manipulability.…”

Section: Background and Related Workmentioning

confidence: 99%

Self-Contained Kinematic Calibration of a Novel Whole-Body Artificial Skin for Human-Robot Collaboration

Watanabe¹,

Strong²,

West³

et al. 2021

Preprint

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In this paper, we present an accelerometer-based kinematic calibration algorithm to accurately estimate the pose of multiple sensor units distributed along a robot body. Our approach is self-contained, can be used on any robot provided with a Denavit-Hartenberg kinematic model, and on any skin equipped with Inertial Measurement Units (IMUs). To validate the proposed method, we first conduct extensive experimentation in simulation and demonstrate a sub-cm positional error from ground truth data-an improvement of six times with respect to prior work; subsequently, we then perform a realworld evaluation on a seven degrees-of-freedom collaborative platform. For this purpose, we additionally introduce a novel design for a stand-alone artificial skin equipped with an IMU for use with the proposed algorithm and a proximity sensor for sensing distance to nearby objects. In conclusion, in this work, we demonstrate seamless integration between a novel hardware design, an accurate calibration method, and preliminary work on applications: the high positional accuracy effectively enables to locate distributed proximity data and allows for a distributed avoidance controller to safely avoid obstacles and people without the need of additional sensing.

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“…Therefore, it is crucial to exploit the robot's perception and avoid relying entirely on open-loop motor commands to perform tasks. Indeed, vision or tactile perception was used in [5,6,7], to correct the kinematics models and improve precision in task execution. Other approaches propose to guide the robotic arm through visual feedback control ( [8,9,10]).…”

Section: Introductionmentioning

confidence: 99%

Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots

Almeida¹,

Vicente²,

Bernardino³

2021

Preprint

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The ability to distinguish between the self and the background is of paramount importance for robotic tasks. The particular case of hands, as the end effectors of a robotic system that more often enter into contact with other elements of the environment, must be perceived and tracked with precision to execute the intended tasks with dexterity and without colliding with obstacles. They are fundamental for several applications, from Human-Robot Interaction tasks to object manipulation. Modern humanoid robots are characterized by high number of degrees of freedom which makes their forward kinematics models very sensitive to uncertainty. Thus, resorting to vision sensing can be the only solution to endow these robots with a good perception of the self, being able to localize their body parts with precision. In this paper, we propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. It is known that CNNs require a huge amount of data to be trained. To overcome the challenge of labeling real-world images, we propose the use of simulated datasets exploiting domain randomization techniques. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy. We focus our attention on developing a methodology that requires low amounts of data to achieve reasonable performance while giving detailed insight on how to properly generate variability in the training dataset. Moreover, we analyze the fine-tuning process within the complex model of Mask-RCNN, understanding which weights should be transferred to the new task of segmenting robot hands. Our final model was trained solely on synthetic images and achieves an average IoU of 82% on synthetic validation data and 56.3% on real test data. These results were achieved with only 1000 training images and 3 hours of training time using a single GPU.

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Robot Self-Calibration Using Multiple Kinematic Chains—A Simulation Study on the iCub Humanoid Robot

Cited by 25 publications

References 30 publications

Calibration of an Elastic Humanoid Upper Body and Efficient Compensation for Motion Planning

Calibration of an Elastic Humanoid Upper Body and Efficient Compensation for Motion Planning

Self-Contained Kinematic Calibration of a Novel Whole-Body Artificial Skin for Human-Robot Collaboration

Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots

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