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.

Studies have shown that the suspended backpack, a wearable device allowing the backpack moving relative to user’s back, can effectively reduce the accelerative vertical force of the backpack to reduce the metabolic cost of users. However, the existing suspended backpack cannot eliminate the accelerative vertical force due to the nonzero suspension stiffness. This paper proposes a constant force suspended backpack adaptable to the varying load to eliminate the accelerative vertical force on the load. To this end, a spring constant force balancing mechanism is designed to achieve near-zero-stiffness suspension. Moreover, a multi-pulley compensation mechanism is proposed for compensating the balance error caused by the pulley diameter to achieve constant force theoretically, and an adjustable mechanism is added to the suspended backpack to nearly achieve constant force balance under different loads. We conducted experiments to validate the efficiency of the constant force suspended backpack. The results demonstrate that the suspended backpack can effectively reduce the maximum net metabolic power of the human by 13.1%, the displacement of the load is reduced by 87.5%, and the peak average acceleration vertical force reduction rate is 88.5%.

Mixed reality technology can give humans an intuitive visual experience, and combined with the multi-source information of the human body, it can provide a comfortable human–robot interaction experience. This paper applies a mixed reality device (Hololens2) to provide interactive communication between the wearer and the wearable robotic limb (supernumerary robotic limb, SRL). Hololens2 can obtain human body information, including eye gaze, hand gestures, voice input, etc. It can also provide feedback information to the wearer through augmented reality and audio output, which is the communication bridge needed in human–robot interaction. Implementing a wearable robotic arm integrated with HoloLens2 is proposed to augment the wearer’s capabilities. Taking two typical practical tasks of cable installation and electrical connector soldering in aircraft manufacturing as examples, the task models and interaction scheme are designed. Finally, human augmentation is evaluated in terms of task completion time statistics.

The Wearable Robotic Limb (WRL) is a type of robotic arm worn on the human body, aiming to enhance the wearer’s operational capabilities. However, proposing additional methods to control and perceive the WRL when human limbs are heavily occupied with primary tasks presents a challenge. Existing interactive methods, such as voice, gaze, and electromyography (EMG), have limitations in control precision and convenience. To address this, we have developed an interactive device that utilizes the mouth and tongue. This device is lightweight and compact, allowing wearers to achieve continuous motion and contact force control of the WRL. By using a tongue controller and mouth gas pressure sensor, wearers can control the WRL while also receiving sensitive contact feedback through changes in mouth pressure. To facilitate bidirectional interaction between the wearer and the WRL, we have devised an algorithm that divides WRL control into motion and force-position hybrid modes. In order to evaluate the performance of the device, we conducted an experiment with ten participants tasked with completing a pin-hole assembly task with the assistance of the WRL system. The results show that the device enables continuous control of the position and contact force of the WRL, with users perceiving feedback through mouth airflow resistance. However, the experiment also revealed some shortcomings of the device, including user fatigue and its impact on breathing. After experimental investigation, it was observed that fatigue levels can decrease with training. Experimental studies have revealed that fatigue levels can decrease with training. Furthermore, the limitations of the device have shown potential for improvement through structural enhancements. Overall, our mouth and tongue interactive device shows promising potential in controlling the WRL during tasks where human limbs are occupied.

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.