With the ongoing research on soft robots, the performance of soft actuators needs to be enhanced for more wide robotic applications. Currently, most soft robots based on pneumatic actuation are capable of assisting small systems, but they are not fully suited for supporting joints requiring large force and range of motion. This is due to the actuation characteristics of the pneumatic artificial muscle (PAM); they are relatively slow, inefficient, and experience a significant force reduction when the PAM contracts. Hence, we propose a novel PAM based on a spring-frame collateral compression mechanism. With only a single compressed air source, the external mesh-covered and inner spring-frame actuators of the proposed PAM operate simultaneously to generate considerable force. Additionally, the design of the internal actuator within the void space of PAM reduces the air consumption and consequently improves the actuator’s operating speed and efficiency. We experimentally confirmed that the proposed PAM exhibited 31.2% greater force, was 25.6% faster, and consumed 21.5% lower air compared to the conventional McKibben muscles. The performance enhancement of the proposed PAM improves the performance of soft robots, allowing the development of more compact robots with greater assistive range.

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.

A conventional blind walking algorithm has low walking stability on uneven terrain because a robot cannot rapidly respond to height changes of the ground due to limited information from foot force sensors. In order to cope with rough terrain, it is essential to obtain 3D ground information. Therefore, this paper proposes a vision-guided six-legged walking algorithm for stable walking on uneven terrain. We obtained noise-filtered 3D ground information by using a Kinect sensor and experimentally derived coordinate transformation information between the Kinect sensor and robot body. While generating landing positions of the six feet from the predefined walking parameters, the proposed algorithm modifies the landing positions in terms of reliability and safety using the obtained 3D ground information. For continuous walking, we also propose a ground merging algorithm and successfully validate the performance of the proposed algorithms through walking experiments on a treadmill with obstacles.

This paper presents the optimized actuator development of the active geometry control suspension(AGCS) system as the world first, unique and patented chassis technology. The aim of this work is cost and weight reduction for actuators of AGCS using the Design For Six Sigma(DFSS) supposing performance must be based on same performance with previous version. To achieve purpose, L18 orthogonal Array Table known as Taguchi Method is used to reduce the number of trial and error in sense of optimization approach. Densign factors of motor and gear(Lead-screw) are selected as a control factor for above concept and thus Control factors have acceptable range as to each design before. For DFSS evaluates the influence-factor and the correlation between selected control factors of actuators in AGCS, a limited number of experiments and the optimized system was verified with simulation and experiment. The test result to optimization procedure showed 1.25 dB gain in S/N ratio and 0.007 gain in beta.