Soft robotics has recently become an emergent research area due to its unique characteristics compared to conventional rigid robots. Its inherent properties, such as compliance and flexibility, provide some promising characteristics for the current robotic applications, including safe human machine interaction, great adaptability to unknown environments and so on. On the contrary, its intrinsic ‘soft’ characteristic would result in some complex non-linear behaviors, causing more difficulties in deducing kinematic or dynamic models of soft robots than what we often do for rigid robots. In this letter, using Finite Element Method (FEM), we demonstrate a gain-scheduling closed-loop method to control a soft trunk robot operating within its workspace. The main idea of this method is to divide the workspace into several sub-workspaces where the most suitable gains are applied correspondingly in each sub-workspace. As a result, it becomes feasible to control the trunk by gain scheduling when crossing from one sub-workspace to another as well as considering its dynamic characteristics. The derivation of the method is presented accordingly. In the end, the proposed method is validated by experimental testing with convincing results provided afterwards.

Pipeline is widely deployed in infrastructures, which raises a great demand of pipe-climbing robots to provide pipe-inside inspecting and platform delivery. Soft pipe-climbing robots (SPRs) are intrinsically flexible and able to operate within complex, narrow and curving pipelines more effectively than their rigid counterparts. In the state of the art, the existing SPRs have demonstrated their performances in fixed-diameter pipes that are placed in static-fluid environments, which however does not show their full potential. In this article, we propose a modular design methodology for SPRs that are expected to operate inside complex pipelines. The robot's torso is composed of three PSAs in parallel, which can realize axial elongation as well as omnidirectional bending with pressurized air. The PSA-based tentacles will elongate after inflation to contact and hold the pipe wall using their silicone pads. Furthermore, the slim tentacles and compact torso results in quite small cross-sectional occupation ratio and subsequently low windward resistance from the liquid in the pipeline, which ensures the robot's high passivity in fluid. The proposed robot can adapt to and work in diverse pipelines with complex fluid conditions, such as horizontal and vertical straight, as well as curved pipes with varying diameters, with and without loads, in still and dynamic water flows. In addition, the robot, whose weight is 50 g, can achieve a fast speed of climbing (up to 15.0 mm/s in open air) and high load-carrying ability (up to 1500 g) due to the quick response of the tentacles and large elongation ratio of its body. The experimental results have proved the high maneuverability of the developed SPR.

Model-based control for continuum robots is often based on the piecewise constant curvature (PCC) assumption. However, the actual physical motions of continuum robots deviate significantly from this assumption, posing challenges to achieving the desired stability and accuracy in control. In this article, a real-time model-based data-driven controller is designed to enhance the precision and stability of continuum robots. An algorithm is designed to construct the static model of the cable-driven continuum robots. A real-time data-based kinematics model is formulated based on the static model, and the multistate motions of the robot are described in this model. A nonlinear control strategy is proposed based on the data-based kinematics model. Validation through simulations and experiments demonstrates the method’s robust stability and impressive precision, achieving a positioning accuracy of 0.0318 mm and a tracking accuracy of 0.0434 mm. This article provides a general paradigm for model-based control of continuum robots, which is crucial to the advancement of this industry.