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.
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.
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