Although a lower extremity exoskeleton shows great prospect in the rehabilitation of the lower limb, it has not yet been widely applied to the clinical rehabilitation of the paralyzed. This is partly caused by insufficient information interactions between the paralyzed and existing exoskeleton that cannot meet the requirements of harmonious control. In this research, a bidirectional human-machine interface including a neurofuzzy controller and an extended physiological proprioception (EPP) feedback system is developed by imitating the biological closed-loop control system of human body. The neurofuzzy controller is built to decode human motion in advance by the fusion of the fuzzy electromyographic signals reflecting human motion intention and the precise proprioception providing joint angular feedback information. It transmits control information from human to exoskeleton, while the EPP feedback system based on haptic stimuli transmits motion information of the exoskeleton back to the human. Joint angle and torque information are transmitted in the form of air pressure to the human body. The real-time bidirectional human-machine interface can help a patient with lower limb paralysis to control the exoskeleton with his/her healthy side and simultaneously perceive motion on the paralyzed side by EPP. The interface rebuilds a closed-loop motion control system for paralyzed patients and realizes harmonious control of the human-machine system.

Shape memory alloy (SMA) has great potential to develop light and compact artificial muscle (AM) due to its muscle-like high power-to-weight ratio, flexibility and silent operation properties. In this paper, SMA self-sensing properties are explored and modeled in depth to imitate the integrated muscle-like functions of actuating and self-sensing for SMA-AM based on the investigation of SMA electrical resistivity (ER). Firstly, an ER transformation kinetics model is proposed based on the simulation of SMA differential scanning calorimetry (DSC) curves. Then a series of thermal-electrical-mechanical experiments are carried out to verify the validity of the ER model, whereby the SMA-AM self-sensing function is well established under different stress conditions. Finally the self-sensing capability is further demonstrated by its application to a novel SMA-AM-actuated active ankle-foot orthosis (AAFO).

Continuous human motion intent learning may be modeled using a Gaussian process (GP) autoregression based evolving system to cope with the unspecified and time-varying motion patterns. Electromyography (EMG) signals are the primary input. GP is used as a mathematical foundation to model human kinematics by adopting the nonlinear autoregressive with exogenous inputs (NARX) framework, and an evolving system is applied to learn the irregular and unspecified dynamic features. The statistical nature of the GP offers superior flexibility for learning human kinematics and is capable of giving credibility to motion intent prediction, which also enables risk-based control. As an important neuromuscular signal, EMG is processed with a novel method, the energy kernel method, to extract the activation level of muscle and feature out muscular force and motion intent. Without losing robustness, the high signal-to-noise ratio or the linearity level with muscular force, huge improvement has been made in computational efficiency to meet the requirements of real-time applications. Experimental works concerning the validity and application of this method are also presented.

A new compact mobile lower limb robotic exoskeleton (MLLRE) has been developed for gait rehabilitation for neurologically impaired patients. This robotic exoskeleton is composed of two exoskeletal orthoses, an active body weight support (BWS) system attached to a motorized mobile base, allowing over-ground walking. The exoskeletal orthosis is optimized to implement the extension and flexion of human hip and knee joints in the sagittal plane. The motor-driven BWS system can actively unload human body weight and track the vertical displacement of the center of mass (COM). This system is compact and easy for therapist to help patient with different weight (up to 100 kg) and height (150–190 cm). Experiments were conducted to evaluate the performance of the robot with a healthy subject. The results show that MLLRE is a useful device for patient to achieve normal over-ground gait patterns.

Locomotion failure is the challenge for wheeled robots applied in granular media, which puts a high demand on the design of wheels. Due to a lack of a mechanical analysis method that can analyze the dynamic interaction between wheel and granular media, it is a challenge to seek design guidance for the wheel geometry. To this end, we introduce a Dynamic Resistive Force Model (DRFM) method suitable for 3D dynamic intrusion. In this method, Granular Resistive Force Theory (RFT) can be extended to 3D RFT smoothly by using granular parameters, and additional velocity terms can describe the effect of intrusion velocity on intrusion force. The relationship between wheel geometry and mechanical properties can be established by performing differential processing on the wheel surface and integrating the force generated by each micro-surface. On this basis, a complete set of solutions is proposed for the design of mobile robot wheels in granular media, and the screw-propelled wheel is taken as an example to optimize it. Theories, simulations, and experiments have all proved that the optimized wheel can produce greater thrust and lift than normal screw-propelled wheel when ensuring the stability of smooth propulsion. The proposed solution and the optimized screw-propelled wheel bring insights for designing mobile robots in granular media.