Precise and large torque generation, back drivability, low output impedance, and compactness of hardware are important requirements for human assistive robots. In this paper, a compact rotary series elastic actuator (cRSEA) is designed considering these requirements. To magnify the torque generated by an electric motor in the limited space of the compact device, a worm gear is utilized. However, the actual torque amplification ratio provided by the worm gear is different from the nominal speed reduction ratio due to friction, which makes the controller design challenging. In this paper, the friction effect is considered in the model of cRSEA, and a robust control algorithm is designed to precisely control the torque output in the presence of nonlinearities such as the friction. The mechanical design and dynamic model of the proposed device and the design of a robust control algorithm are discussed, and actuation performance is verified by experiments. Experimental results with a human subject are also presented to show the performance of the cRSEA while interacting with humans.

A series elastic actuator (SEA) is a promising actuation method in force control applications that intelligently interacts with environments. The SEA is characterized by a spring placed between a load and an actuator, which is an electric motor in most cases. Since the spring plays the role of a transducer between position (i.e., the spring deflection) and force, it is able to control the output force (torque) precisely by utilizing typical position control methods. However, the force control performance of the SEA is considered to have limitations due to its elasticity, and thus, to be inferior to rigid actuators in terms of bandwidth. This paper proposes that the force control performance of the SEA can be improved by exploiting the dynamic model of the SEA. To this end, the SEA is modeled and analyzed utilizing the two-mass dynamic model, which is a well-known and widely accepted model of the flexible system. The disturbance observer and feedforward controller are introduced as the model-based control algorithms for the SEA to achieve the high-precision force control. In addition to high-bandwidth force control, the proposed controller can address the robust stability and performance against model parameter variance and exogenous disturbances. For the analytic and quantitative assessment of the proposed force control system, the dynamic characteristics of an SEA under various control algorithms are analyzed, and the experimental results are provided for an actual SEA system in this paper.

This paper introduces a robotic biopsy device for capsule endoscopes. The proposed device consists of three modules for the complete process of biopsy, which includes monitoring the intestinal wall by a tissue monitoring module (TMM), aligning onto a polyp by an anchor module (AM), and sampling of the polyp tissue by a biopsy module (BM). The TMM utilizes a trigonal mirror as well as an on-board camera; since the TMM continuously takes images through lateral apertures, an operator such as a medical doctor is able to anchor the capsule endoscope onto the polyp and biopsy it with the visual feedback in real-time. When the operator finds a polyp using the TMM and the frontal camera of a capsule endoscope, then the AM is used to approach the polyp for biopsy. When the AM is in use, outriggers are extruded by shape-memory-alloy (SMA) springs, which results in the swelling of capsule endoscope body. In addition, an alignment module, which is a part of the AM, rotates the body of the capsule endoscope such that the biopsy razor can be aligned onto the polyp. Then, the BM excises a part of the polyp and seals the aperture, and the capsule endoscope continues exploring the intestine. The concept and working principles of the proposed device are introduced in this paper and are verified by a prototype that successfully integrates the three modules.

Gait modes, such as level walking, stair ascent/descent, and ramp ascent/descent, show different lower-limb kinematic and kinetic characteristics. Therefore, an accurate detection of these modes is critical for a wearable robot to provide appropriate power assistance. In this paper, a fast gait-mode-detection method based on a body sensor system is proposed. A fuzzy logic algorithm is used to estimate the likelihoods of gait modes in real time. Since the proposed fast gait mode detection makes it possible to select appropriate kinematic and kinetic models for each gait mode, assistive torques required for assisting the human motions can be obtained more naturally and immediately. The proposed methods are all verified by experiments with a lower-limb exoskeletal assistive robot with transparent actuation by series elastic actuators, called the exoskeletal robotic orthosis for walking assistance. Four healthy subjects participated in the experiments. All subjects were asked to perform different gait modes using their normal and simulated abnormal gaits, i.e., blocking the knee joint of one leg during walking. Latency and success rate of gait mode detection are selected as performance criteria. The effectiveness of the proposed gait-mode-based assistive strategy is evaluated using electromyography muscular activities.