The field of soft manipulators requires a more promising solution, including efficient structures and controllers. This paper presents a novel cable–pneumatic hybrid-driven tapered soft manipulator (TSM) design and control scheme to enhance the performance in actual tasks. This paper is the first to present the design with a Bowden tube as a driving tendon and propose a composite tendon with Bowden tubes and cable tendons (BTCTs). Leveraging the principles of hybrid-driven antagonism, the compact TSM integrates the composite tendon with BTCTs and pneumatically actuated tapered bellows (PATBs). This new hybrid-driven form provides the TSM with excellent resistance to axial extension, tangential bending, and torsion, enhancing the stiffness of the TSM. The variable-stiffness range of the TSM was quantified in tests including axial stiffness (0.57–10.77 N/mm), tangential bending stiffness (0.01–0.45 N/mm), and torsion stiffness (0.02–0.044 Nm/deg) tests. A deep learning-based neural network (NN) approach was utilized to model the inverse kinematics of the TSM. For more precise motion control, using position and orientation feedback from the sensor at the tip, we have designed a closed-loop iterative feedback controller (IFC) incorporating three algorithms. Experiments on spatial point positioning, trajectory tracking with different constraints, orientation control, and disturbance experiments were conducted on the TSM. Experimental results (spatial point positioning error (mean error of stable region: 0.17 mm), circular trajectory tracking error (mean and standard deviation of 100 trials: 0.87 $\pm$ 0.57 mm), orientation control error (less than 1 $^{\circ }$ ), and the performance in disturbance experiment) demonstrated that our approach has high control accuracy and strong robustness against external disturbances. We conducted experiments involving teleoperation control, collision-free precise operations in cluttered and constrained environments, and disturbance-adaptive board cleaning testing, ensuring both stability and safety during contact with humans. These experiments intuitively demonstrate the potential of this TSM for executing complex tasks in real-world environments, promising to become a safe collaborative assistant for humans in the future.

Supernumerary robotic limbs (SRL) are new types of wearable robots used as the third limb to work with humans. The device is designed to provide the wearer with better auxiliary ability. This paper presents the design and implementation of a Series-Parallel-Reconfigurable Tendon-driven Supernumerary Robotic Arms (RTSRAs), which is lightweight, compact and Multi-assistive for the wearers. Existing Supernumerary robotic limbs (SRL) devices almost use series configurations with high flexibility, but relatively poor carrying capacity compared to parallel configurations. Balancing the contradiction between load capacity and flexibility, we propose a Series-Parallel-Reconfigurable configuration requiring clever end-conversion tools to better adapt to variable tasks. To achieve the compactness of RTSRAs, two kinds of tendon-driven joint module were designed. The coupled tendon-driven joint module was applied to transmit the power of motors mounted near the backboard to the elbow joints, which greatly reduce the motion inertia and increase load capacity of RTSRAs. Finally, on the basis of forward and inverse kinematics analysis, we make the evaluation of RTSRAs. The proposed RTSRAs has been successfully applied in typical load operation scenario and suggests a great potential in future applications.

This article introduces the electroadhesion-stiffening self-adaptive soft robotic gripper (EA-SoGripper), a novel solution to the limitations of traditional rigid robotic grippers. The EA-SoGripper leverages electroadhesion (EA) clutches to dynamically adjust the tension of films, enabling adaptive grasping for objects of varying shapes, sizes, weights, and stiffness. By controlling the applied voltage, the gripper achieves tunable grasping stiffness, with a range of 0.06–1.25 N/mm. A mathematical model predicting load capacity as a function of stiffness and input voltage offers theoretical guidance for practical applications. The gripper integrates a real-time force sensor and a feedback control algorithm, achieving grasping force regulation with high precision. A portable, low-cost ($130), and lightweight (197 g) ac high-voltage power supply system, with a fast response time of 75 ms, further enhances its versatility. Experimental validation demonstrates the EA-SoGripper's ability to securely grasp objects with diverse geometries, including fragile and delicate items, while adapting stiffness to support up to 400 g loads. These results highlight the gripper's potential for applications requiring precise and adaptive handling.

Supernumerary robotic limbs (SRLs) are wearable robots that serve as extra limbs, assisting wearers in task completion. The strong coupling between SRLs and wearers results in wearer movements influencing SRLs' actions and the forces exerted on mechanisms during operations. The wearer's movements lead to uncertain and uncontrollable base movements, making operational control of the SRLs challenging. This study proposes a full-process assisted door-opening method using SRLs, integrating a task model and an anti-disturbance door-opening controller. Multimodal information from the SRLs system is utilized to facilitate task state transitions. The anti-disturbance door-opening controller consists of compliant controller, online identification of mechanism characteristics, end-effector trajectory generator, and notch filter. This method enables the SRLs to complete the door-opening task under motion disturbances caused by the wearer, without prior knowledge of the door's resistance, radius, or axis direction. Experimental results show that the SRLs can successfully open doors, with estimates converging to actual values under various radius and axis direction conditions (with radius estimation error less than 0.01m and axial estimation error less than 3.8 $^{\circ }$ ). The effectiveness of the proposed control framework is validated in daily environments.

Estimating the contact forces and moments (CFMs) between exoskeletons’ feet and the ground is a prerequisite for calculating exoskeletons’ joint moments. However, comfortable, portable, and high-precision force sensors for CFM detection are difficult to design and manufacture. In addition, there are many unknown CFM components (six force components and six moment components in the double-support phase). These reasons make it challenging to estimate CFMs precisely. In this paper, we propose a novel method for estimating these CFMs based on a proposed dynamic decoupled coordinate system (DDCS) and the minimum energy hypothesis. By decomposing these CFMs into a DDCS, the number of unknowns can be significantly reduced from twelve to two. Meanwhile, the minimum energy hypothesis provides a relatively reliable target for optimizing the remaining two unknown variables. We verify the accuracy of this method using a public data set about human walking. The validation shows that the proposed method is capable of estimating CFMs. This study provides a practical way to estimate the CFMs under the soles, which contributes to reducing the research and development costs of exoskeletons by avoiding the need for expensive plantar sensors. The sensor-free approach also reduces the dependence on high-precision, portable, and comfortable CFM detection sensors, which are usually difficult to design.