Design optimization of soft pneumatic actuators using genetic algorithms

Runge, Gundula; Peters, Jan; Raatz, Annika

doi:10.1109/robio.2017.8324449

Cited by 19 publications

(11 citation statements)

References 19 publications

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“…The genetic algorithm resembles the process of natural selection, in which the fittest individuals survive to produce the offspring of the next generation (29). Different from gradient-based algorithms that may provide only locally optimized results, the genetic algorithm has been widely used in many fields because of its remarkable efficiency in seeking near-global optimum in large search spaces (39)(40)(41)(42)(43).…”

Section: [3]mentioning

confidence: 99%

Evolutionary design of magnetic soft continuum robots

Wang

Zheng

Harker

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

106

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Worldwide cardiovascular diseases such as stroke and heart disease are the leading cause of mortality. While guidewire/catheter-based minimally invasive surgery is used to treat a variety of cardiovascular disorders, existing passive guidewires and catheters suffer from several limitations such as low steerability and vessel access through complex geometry of vasculatures and imaging-related accumulation of radiation to both patients and operating surgeons. To address these limitations, magnetic soft continuum robots (MSCRs) in the form of magnetic field–controllable elastomeric fibers have recently demonstrated enhanced steerability under remotely applied magnetic fields. While the steerability of an MSCR largely relies on its workspace—the set of attainable points by its end effector—existing MSCRs based on embedding permanent magnets or uniformly dispersing magnetic particles in polymer matrices still cannot give optimal workspaces. The design and optimization of MSCRs have been challenging because of the lack of efficient tools. Here, we report a systematic set of model-based evolutionary design, fabrication, and experimental validation of an MSCR with a counterintuitive nonuniform distribution of magnetic particles to achieve an unprecedented workspace. The proposed MSCR design is enabled by integrating a theoretical model and the genetic algorithm. The current work not only achieves the optimal workspace for MSCRs but also provides a powerful tool for the efficient design and optimization of future magnetic soft robots and actuators.

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Section: [3]mentioning

confidence: 99%

Evolutionary design of magnetic soft continuum robots

Wang

Zheng

Harker

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

106

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show abstract

“…Design optimization of a six chambered soft robot in Guo and Kang (2020) was performed using the non-linear programming by quadratic Lagrangian (NLPQL) coupled with Finite Element (FE) modeling. A genetic algorithm was utilized in the multiobjective design optimization of a soft multi-DOF manipulator (Bodily, 2017) and in the design optimization of a soft pneumatic actuator (Runge et al, 2017). In design optimizations, the algorithms that can handle non-linear and high dimensionality problems are favorable, due to complex multi-parameter structure-material relations present in soft robotic systems.…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of a Pneumatic Soft Robotic Actuator Using Model-Based Optimization and Deep Reinforcement Learning

et al. 2021

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We present two frameworks for design optimization of a multi-chamber pneumatic-driven soft actuator to optimize its mechanical performance. The design goal is to achieve maximal horizontal motion of the top surface of the actuator with a minimum effect on its vertical motion. The parametric shape and layout of air chambers are optimized individually with the firefly algorithm and a deep reinforcement learning approach using both a model-based formulation and finite element analysis. The presented modeling approach extends the analytical formulations for tapered and thickened cantilever beams connected in a structure with virtual spring elements. The deep reinforcement learning-based approach is combined with both the model- and finite element-based environments to fully explore the design space and for comparison and cross-validation purposes. The two-chamber soft actuator was specifically designed to be integrated as a modular element into a soft robotic pad system used for pressure injury prevention, where local control of planar displacements can be advantageous to mitigate the risk of pressure injuries and blisters by minimizing shear forces at the skin-pad contact. A comparison of the results shows that designs achieved using the deep reinforcement based approach best decouples the horizontal and vertical motions, while producing the necessary displacement for the intended application. The results from optimizations were compared computationally and experimentally to the empirically obtained design in the existing literature to validate the optimized design and methodology.

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“…This paper presents a generalized pattern search to optimize concentric tube robots while they can reach the target point with minimal curvature and length. Runge et al [ 15 ] used evolutionary algorithms such as genetic algorithm to optimize a soft robot. Bodily et al [ 16 ] used a genetic algorithm to optimize the reachability, dexterity, and manipulability of a multisegment continuum robot.…”

Section: Introductionmentioning

confidence: 99%

Inverse Kinematics of Concentric Tube Robots in the Presence of Environmental Constraints

Jabari

Zakeri

Janabi–Sharifi

et al. 2021

Applied Bionics and Biomechanics

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Inverse kinematics (IK) of concentric tube continuum robots (CTRs) is associated with two main problems. First, the robot model (e.g., the relationship between the configuration space parameters and the robot end-effector) is not linear. Second, multiple solutions for the IK are available. This paper presents a general approach to solve the IK of CTRs in the presence of constrained environments. It is assumed that the distal tube of the CTR is inserted into a cavity while its proximal end is placed inside a tube resembling the vessel enabling the entry to the organ cavity. The robot-tissue interaction at the beginning of the organ-cavity imposed displacement and force constraints to the IK problem to secure a safe interaction between the robot and tissue. The IK in CTRs has been carried out by treating the problem as an optimization problem. To find the optimized IK of the CTR, the cost function is defined to be the minimization of input force into the body cavity and the occupied area by the robot shaft body. The optimization results show that CTRs can keep the safe force range in interaction with tissue for the specified trajectories of the distal tube. Various simulation scenarios are conducted to validate the approach. Using the IK obtained from the presented approach, the tracking accuracy is achieved as 0.01 mm which is acceptable for the application.

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Design optimization of soft pneumatic actuators using genetic algorithms

Cited by 19 publications

References 19 publications

Evolutionary design of magnetic soft continuum robots

Evolutionary design of magnetic soft continuum robots

Design Optimization of a Pneumatic Soft Robotic Actuator Using Model-Based Optimization and Deep Reinforcement Learning

Inverse Kinematics of Concentric Tube Robots in the Presence of Environmental Constraints

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