The twisted string actuator (TSA) has been extensively studied as an alternative to metallic gear reducers due to its low-cost, lightweight, compliance, and highly adjustable actuation characteristics. Owing to these advantages, TSAs were utilized to realize lightweight human-robot interactive robots requiring safe interaction, such as wearables and prosthetics, at a relatively low cost. However, because its force transmission ratio is almost proportional to the length of the string, the string of the conventional TSA must be elongated to achieve high force output. Thus, it is difficult to obtain high force capacity while maintaining a small size. To overcome this limitation, we propose a novel TSA based on parallel bundle-driven actuation to significantly reduce the length of the TSA while achieving high force output. Furthermore, in order to boost the proposed TSA performance, we also present a passive mechanism of an asymmetric behavior compensator. The experimental results show that the force transmission ratio of the proposed TSA is increased by 21%, compared with that of the conventional TSA. Consequently, the proposed TSA has a 44% higher torque capacity with a 37% shorter string than those of the conventional TSA.

As robots are becoming more accessible in our daily lives, the interest in physical human–robot interaction (HRI) is rapidly increasing. An electric bicycle (E-bike) is one of the best examples of HRI, because a rider simultaneously actuates the rear wheel of the E-bike in close proximity. Most commercially available E-bikes employ a control methodology known as a power assistant system (PAS). However, this type of system cannot offer fully efficient power assistance for E-bikes since it does not account for the biomechanics of riders. In order to address this issue, we propose a control algorithm to increase the efficiency and enhance the riding experience of E-bikes by implementing the control parameters acquired from analyses of human leg kinematics and muscular dynamics. To validate the proposed algorithm, we have evaluated and compared the performance of E-bikes in three different conditions: (1) without power assistance, (2) assistance with a PAS algorithm, and (3) assistance with the proposed algorithm. Our algorithm required 5.09% less human energy consumption than the PAS algorithm and 11.01% less energy consumption than a bicycle operated without power assistance. Our algorithm also increased velocity stability by 11.89% and acceleration stability by 27.28%, and decreased jerk by 12.36% in comparison to the PAS algorithm.