Model-Based Shape Estimation for Soft Robotic Manipulators: The Planar Case

Trivedi, Deepak; Rahn, Christopher D.

doi:10.1115/1.4026338

Cited by 35 publications

(23 citation statements)

References 22 publications

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“…For a module with three actuators we have [3], -Euler-Bernoulli Beam Model: For pneumatic actuators, a second step is needed to map the pressure inputs to actuator length. In the case of the well-known "OCT-Arm" series of continuum robots [17] where the modules are created from actuators without a supporting shell, a map between the actuator pressure and elongation is needed. A simple model can be derived by assuming the manipulator deformation as superposing of the elongation and bending of an Euler-Bernoulli beam in the direction (Eq.…”

Section: Constant Curvature Kinematics and Mechanicsmentioning

confidence: 99%

Mechanics of Continuum Manipulators, a Comparative Study of Five Methods with Experiments

Sadati

Naghibi

Shiva

et al. 2017

Towards Autonomous Robotic Systems

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Abstract.: Investigations on control and optimization of continuum manipulators have resulted in a number of kinematic and dynamic modeling approaches each having their own advantages and limitations in various applications. In this paper, a comparative study of five main methods in the literature for kinematic, static and dynamic modeling of continuum manipulators is presented in a unified mathematical framework. The five widely used methods of Lumped system dynamic model, Constant curvature, two-step modified constant curvature, variable curvature Cosserat rod and beam theory approach, and series solution identification are re-viewed here with derivation details in order to clarify their methodological differences. A comparison between computer simulations and experimental results using a STIFF-FLOP continuum manipulator is presented to study the advantages of each modeling method.

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Section: Constant Curvature Kinematics and Mechanicsmentioning

confidence: 99%

Mechanics of Continuum Manipulators, a Comparative Study of Five Methods with Experiments

Sadati

Naghibi

Shiva

et al. 2017

Towards Autonomous Robotic Systems

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“…In Trivedi and Rahn ( 2009 ) and Trivedi and Rahn ( 2014 ), the authors solve for the configuration of the OctArm robot platform with unknown payloads using Cosserat rod models and three different sensing methods (e.g., force-torque sensors and an inclinometer at the base, multiple cable encoders, and multiple inclinometers along the manipulator) to constrain and solve initial value or ODE problems with given boundary conditions. The method was effective, but required varying levels of accurate knowledge about soft robot parameters depending on the sensing method used and was again restricted to planar applications (although not due to limits in formulation).…”

Section: Introductionmentioning

confidence: 99%

Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement

2021

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This paper presents methods for placing length sensors on a soft continuum robot joint as well as a novel configuration estimation method that drastically minimizes configuration estimation error. The methods utilized for placing sensors along the length of the joint include a single joint length sensor, sensors lined end-to-end, sensors that overlap according to a heuristic, and sensors that are placed by an optimization that we describe in this paper. The methods of configuration estimation include directly relating sensor length to a segment of the joint's angle, using an equal weighting of overlapping sensors that cover a joint segment, and using a weighted linear combination of all sensors on the continuum joint. The weights for the linear combination method are determined using robust linear regression. Using a kinematic simulation we show that placing three or more overlapping sensors and estimating the configuration with a linear combination of sensors resulted in a median error of 0.026% of the max range of motion or less. This is over a 500 times improvement as compared to using a single sensor to estimate the joint configuration. This error was computed across 80 simulated robots of different lengths and ranges of motion. We also found that the fully optimized sensor placement performed only marginally better than the placement of sensors according to the heuristic. This suggests that the use of a linear combination of sensors, with weights found using linear regression is more important than the placement of the overlapping sensors. Further, using the heuristic significantly simplifies the application of these techniques when designing for hardware.

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“…Regarding position control of soft and continuum manipulators, some research studies propose the use of mechanical models that consider the deformation of the material and mechanical behaviour of the robot. Three main categories have been used: Piecewise Constant Curvature (PCC) [23], [24], classical Cosserat [25], [26], [27], and Finite Elements Models (FEM) [28]. Challenges remain, when applied to real physical robotics manipulators, with respect to real-time computation and accurate description of the kinematics, in particular, when external forces are exerted to a robot.…”

Section: Introductionmentioning

confidence: 99%

Open-Loop Position Control in Collaborative, Modular Variable-Stiffness-Link (VSL) Robots

Gandarias

Wang

Stilli

et al. 2020

IEEE Robot. Autom. Lett.

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Collaborative robots (cobots) open up new avenues in the fields of industrial robotics and physical Human-Robot Interaction (pHRI) as they are suitable to work in close approximation and in collaboration with humans. The integration and control of variable stiffness elements allow inherently safe interaction. Apart from notable work on Variable Stiffness Actuators, the concept of Variable-Stiffness-Link (VSL) manipulators promises safety improvements in cases of unintentional physical collisions. However, position control of these type of robotic manipulators is challenging for critical task-oriented motions (e.g., pick and place). Hence, the study of open-loop position control for VSL robots is crucial to achieve high levels of safety, accuracy and hardware cost-efficiency in pHRI applications. In this paper, we propose a hybrid, learning based kinematic modelling approach to improve the performance of traditional open-loop position controllers for a modular, collaborative VSL robot. We show that our approach improves the performance of traditional open-loop position controllers for robots with VSL and compensates for position errors, in particular, for lower stiffness values inside the links: Using our upgraded and modular robot, two experiments have been carried out to evaluate the behaviour of the robot during taskoriented motions. Results show that traditional model-based kinematics are not able to accurately control the position of the end-effector: the position error increases with higher loads and lower pressures inside the VSLs. On the other hand, we demonstrate that, using our approach, the VSL robot can outperform the position control compared to a robotic manipulator with 3D printed rigid links.

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Model-Based Shape Estimation for Soft Robotic Manipulators: The Planar Case

Cited by 35 publications

References 22 publications

Mechanics of Continuum Manipulators, a Comparative Study of Five Methods with Experiments

Mechanics of Continuum Manipulators, a Comparative Study of Five Methods with Experiments

Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement

Open-Loop Position Control in Collaborative, Modular Variable-Stiffness-Link (VSL) Robots

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