ROMANSY 21 - Robot Design, Dynamics and Control

Parenti‐Castelli, Vincenzo; Schiehlen, Werner

doi:10.1007/978-3-319-33714-2

Cited by 4 publications

(2 citation statements)

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“…In [13] locally weighted regression (LWR) was used to learn the forward kinematic model of a robot and then solve the inverse kinematics problem at the velocity level. Thuruthel et al [14] employed neural networks to learn the inverse kinematics of a soft robot directly, whereas Xu et al [15] compared different approaches (Gaussian mixture models, k-nearest neighbour regression, and extreme machine learning) to model the inverse kinematics of a cable-robot.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, learning methods can also be divided into local or global. Global methods, such as those in [11,12,14,15], based on Artificial Neural Networks (ANN) or Gaussian process regression allow finding an input-output mapping using all observed data. Local methods such as those proposed in [13,16], conversely, find local approximations [10].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Kinematic Modelling for Multiobjective Control of a Redundant Surgical Robotic Tool

2020

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Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniques are a preferred approach to tackle this problem. However, surgical environments are rarely structured, due to organs being very soft and deformable, and unpredictable, for instance, because of fluids in the system, wear and break of the tendons that lead to changes of the system’s behaviour. Therefore, the model needs to quickly adapt. In this work, we propose a method to learn the kinematic model of a redundant surgical robot and control it to perform surgical tasks both autonomously and in teleoperation. The approach employs Feedforward Artificial Neural Networks (ANN) for building the kinematic model of the robot offline, and an online adaptive strategy in order to allow the system to conform to the changing environment. To prove the capabilities of the method, a comparison with a simple feedback controller for autonomous tracking is carried out. Simulation results show that the proposed method is capable of achieving very small tracking errors, even when unpredicted changes in the system occur, such as broken joints. The method proved effective also in guaranteeing accurate tracking in teleoperation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Kinematic Modelling for Multiobjective Control of a Redundant Surgical Robotic Tool

2020

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Implementation of the Adaptive Control Algorithm for the KUKA LWR 4+ Robot

Woliński

2018

Springer Proceedings in Mathematics &Amp; Statistics

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Vehicle Localization Using 3D Building Models and Point Cloud Matching

Ballardini

Fontana

Cattaneo

et al. 2021

Sensors

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Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.

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ROMANSY 21 - Robot Design, Dynamics and Control

Cited by 4 publications

References 173 publications

Adaptive Kinematic Modelling for Multiobjective Control of a Redundant Surgical Robotic Tool

Adaptive Kinematic Modelling for Multiobjective Control of a Redundant Surgical Robotic Tool

Implementation of the Adaptive Control Algorithm for the KUKA LWR 4+ Robot

Vehicle Localization Using 3D Building Models and Point Cloud Matching

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