A feed-forward neural network learning the inverse kinetics of a soft cable-driven manipulator moving in three-dimensional space

Giorelli, Michele; Renda, Federico; Ferri, Gabriele; Laschi, Cecilia

doi:10.1109/iros.2013.6697084

Cited by 93 publications

(69 citation statements)

References 23 publications

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“…Giorelli et al [12] learned a force based inverse model of a redundant cable driven manipulator. There, an MLP network is employed to overcome the problems regarding the generation of a Jacobi matrix for a highly redundant manipulator.…”

Section: Introductionmentioning

confidence: 99%

Population based Mean of Multiple Computations networks: A building block for kinematic models

Baum

Meier

Schilling

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Population based encodings allow to represent probabilistic and fuzzy state estimates. Such a representation will be introduced and applied for the case of a redundant manipulator. Following the Mean of Multiple Computations principle, a neural network model (PbMMC) is presented in which the overall complexity is divided into multiple local relationships. This allows to solve inverse, forward and mixed kinematic problems. The local transformations in between the kinematic variables can be sufficiently well learned by small single MLP layers. The population codes of the kinematic variables are based on nested periodic receptive fields which allow to express multiple weighted state estimates. Therefore, the model as such is quite flexible as it can keep track of multiple possible solutions at the same time.

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Section: Introductionmentioning

confidence: 99%

Population based Mean of Multiple Computations networks: A building block for kinematic models

Baum

Meier

Schilling

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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“…There are a number of methods for modeling tendon drive continuum robots, and these allow non-constantly curvature of manipulators to be estimated by considering the inherent torsion of the manipulator . Methods for modeling tendon-driven manipulators include Jacobian methods (Giorelli et al, 2013) and neural network approaches (Giorelli et al, 2013).…”

Section: Modeling and Simulationmentioning

confidence: 99%

Soft Manipulators and Grippers: A Review

et al. 2016

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Soft robotics is a growing area of research which utilizes the compliance and adaptability of soft structures to develop highly adaptive robotics for soft interactions. One area in which soft robotics has the ability to make significant impact is in the development of soft grippers and manipulators. With an increased requirement for automation, robotics systems are required to perform task in unstructured and not well defined environments; conditions which conventional rigid robotics are not best suited. This requires a paradigm shift in the methods and materials used to develop robots such that they can adapt to and work safely in human environments. One solution to this is soft robotics, which enables soft interactions with the surroundings while maintaining the ability to apply significant force. This review paper assesses the current materials and methods, actuation methods and sensors which are used in the development of soft manipulators. The achievements and shortcomings of recent technology in these key areas are evaluated, and this paper concludes with a discussion on the potential impacts of soft manipulators on industry and society.

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“…Immega and Antonelli 17 proposed a cascaded spline arc approach that controls the cables of the KSI tentacle that roughly positions it in 3-D space, which is further improved through a closed-loop vision feedback. Giorelli et al 24 used a Jacobian based approach to reach an average tip accuracy of 6% of the total manipulator length. Marchese et al 31 applied a closed-loop controller on a 3D soft-arm to position the end effector to reach a ball with a diameter of 0.04m.…”

Section: Challenges In Controlmentioning

confidence: 99%

“…These traditional model-based methods are limited in accuracy and computational costs by modelling assumptions that need to be improved for practical application of soft manipulators. Learning based control 23,24,26 is a promising approach to automate low-level sensorimotor skills. The underlying principle is to allow the manipulator to autonomously explore its environment and correlate sensory and motor spaces.…”

Section: Challenges In Controlmentioning

confidence: 99%

Towards the development of a soft manipulator as an assistive robot for personal care of elderly people

Ansari

Manti

Falotico

et al. 2017

International Journal of Advanced Robotic Systems

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Manipulators based on soft robotic technologies exhibit compliance and dexterity which ensures safe human-robot interaction. This article is a novel attempt at exploiting these desirable properties to develop a manipulator for an assistive application, in particular, a shower arm to assist the elderly in the bathing task. The overall vision for the soft manipulator is to concatenate three modules in a serial manner such that (i) the proximal segment is made up of cable-based actuation to compensate for gravitational effects and (ii) the central and distal segments are made up of hybrid actuation to autonomously reach delicate body parts to perform the main tasks related to bathing. The role of the latter modules is crucial to the application of the system in the bathing task; however, it is a nontrivial challenge to develop a robust and controllable hybrid actuated system with advanced manipulation capabilities and hence, the focus of this article. We first introduce our design and experimentally characterize its functionalities, which include elongation, shortening, omnidirectional bending. Next, we propose a control concept capable of solving the inverse kinetics problem using multiagent reinforcement learning to exploit these functionalities despite high dimensionality and redundancy. We demonstrate the effectiveness of the design and control of this module by demonstrating an open-loop task space control where it successfully moves through an asymmetric 3-D trajectory sampled at 12 points with an average reaching accuracy of 0.79 cm + 0.18 cm. Our quantitative experimental results present a promising step toward the development of the soft manipulator eventually contributing to the advancement of soft robotics.

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A feed-forward neural network learning the inverse kinetics of a soft cable-driven manipulator moving in three-dimensional space

Cited by 93 publications

References 23 publications

Population based Mean of Multiple Computations networks: A building block for kinematic models

Population based Mean of Multiple Computations networks: A building block for kinematic models

Soft Manipulators and Grippers: A Review

Towards the development of a soft manipulator as an assistive robot for personal care of elderly people

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