Soft continuum manipulators offer levels of compliance and inherent safety that can render them a superior alternative to conventional rigid robots for a variety of tasks, such as medical interventions or human–robot interaction. However, the ability of soft continuum manipulators to compensate for external disturbances needs to be further enhanced to meet the stringent requirements of many practical applications. In this paper, we investigate the control problem for soft continuum manipulators that consist of one inextensible segment of constant section, which bends under the effect of the internal pressure and is subject to unknown disturbances acting in the plane of bending. A rigid-link model of the manipulator with a single input pressure is employed for control purposes and an energy-shaping approach is proposed to derive the control law. A method for the adaptive estimation of disturbances is detailed and a disturbance compensation strategy is proposed. Finally, the effectiveness of the controller is demonstrated with simulations and with experiments on an inextensible soft continuum manipulator that employs pneumatic actuation.

Soft robotic manipulators with fluidic actuation are devices with easily deformable structures that comprise a set of chambers that can be pressurized to achieve structural deflection. These devices have experienced a rapid development in recent years, which is not least due to the advantages they offer in terms of robustness, affordability, and compliance. Nowadays, however, soft robotic manipulators are designed mostly by intuition, which complicates design improvement and hampers the advancement of the field. In this article, a general study of the design of soft robotic manipulators with fluidic actuation is presented using an analytical derivation. The study relies on a novel approach that is applicable to a general design and thus provides a common framework for the design of soft robots. In the study, two design layouts of interest are first justified, which correspond to extending and contracting devices. Design principles for each of the layouts are subsequently derived, both for planar and 3D scenarios, and considering operation to support any external loading and to provide any desired deflection. These principles are found to agree with the main design trends in the literature, although they also highlight the potential for improvement in specific aspects of the design geometry and stiffness distribution. The principles are used to identify the most suitable design for both extending and contracting devices in 2D and 3D and extract insight into their behavior. To showcase the use of these design principles, a prototypical scenario in minimally invasive surgery requiring a manipulator segment capable of bending in any direction is defined, where the objective is to maximize its lateral force. The principles are applied to determine the most suitable design. These also highlight the need for numerical analysis to optimize two design parameters. Finite element simulations are developed, and their results are reported. Among the most relevant is the fact that the cross-sectional area with pressurized fluid should be maximized and that the stiffness in the design should be selected to minimize structural stretching. The simulations yield the optimal design, which offers higher force than existing, reference ones. The simulations also provide verification for the study.

Abstract-Steerable needles are a promising technology for minimally invasive surgery, as they can provide access to difficult to reach locations while avoiding delicate anatomical regions. However, due to the unpredictable tissue deformation associated with needle insertion and the complexity of many surgical scenarios, a real-time path planning algorithm with high update frequency would be advantageous. Real-time path planning for nonholonomic systems is commonly used in a broad variety of fields, ranging from aerospace to submarine navigation. In this paper, we propose to take advantage of the architecture of Graphics Processing Units (GPUs) to apply fractal theory and thus parallelize real-time path planning computation. This novel approach, termed Adaptive Fractal Trees (AFT), allows for the creation of a database of paths covering the entire domain, which are dense, invariant, procedurally produced, adaptable in size, and present a recursive structure. The generated cache of paths can in turn be analyzed in parallel to determine the most suitable path in a fraction of a second. The ability to cope with nonholonomic constraints, as well as constraints in the space of states of any complexity or number, is intrinsic to the AFT approach, rendering it highly versatile. Three-dimensional simulations applied to needle steering in neurosurgery show that our approach can successfully compute paths in real-time, enabling complex brain navigation.

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