This paper proposes a 3-D biped dynamic walking algorithm based on passive dynamic autonomous control (PDAC). The robot dynamics is modeled as an autonomous system of a 3-D inverted pendulum by applying the PDAC concept that is based on the assumption of point contact of the robot foot and the virtual constraint as to robot joints. Due to autonomy, there are two conservative quantities named "PDAC constant," which determine the velocity and direction of the biped walking. We also propose the convergence algorithm to make PDAC constants converge to arbitrary values, so that walking velocity and direction are controllable. Finally, experimental results validate the performance and the energy efficiency of the proposed algorithm.

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An environmentally specific type of locomotion (e.g., bipedal or quadrupedal walking) is effective only under the specified environments. However, other conditions could cause physical body constraints and decrease mobility. Despite these constraints, legged robots are desired with high overall mobility such that they can walk under various conditions. Thus, a combination of types of locomotion is needed to maximize overall mobility. We have developed a gorilla-type robot, which can switch between bipedal and quadrupedal walking. A selection technique to optimize locomotion choice would be beneficial to the robot, which will experience challenging situations when walking through complex terrains, receiving disturbances, or malfunctioning. We present a selection algorithm for locomotion (SAL) that improves overall mobility by autonomously selecting the optimal locomotion. The falling risk of each locomotion mode is evaluated with a Bayesian network to represent the robot's situation. The evaluation function for the SAL determines the optimal locomotion choice based on falling risk and moving speed. In this paper, the SAL is used for two state variables of locomotion: gait (Ga-SAL) and speed (Sp-SAL). Both the simulations and experiments validated that the robot traveled efficiently in complex environments.Index Terms-Biomimetics, falling risk, learning and adaptive systems, legged robots, selection algorithm for locomotion (SAL).

Endoscopic endonasal surgery (EES) is a minimally invasive technique for removal of pituitary adenomas or cysts at the skull base. This approach can reduce the invasiveness and recovery time compared to traditional open surgery techniques. However, it represents challenges to surgeons because of the constrained workspace imposed by the nasal cavity and the lack of dexterity with conventional surgical instruments. While robotic surgical systems have been previously proposed for EES, issues concerned with proper interface design still remain. In this paper, we present a cooperative, compact, and versatile bimanual human-robot interface aimed to provide intuitive and safe operation in robot-assisted EES. The proposed interface is attached to a robot arm and holds a multi-degree-of-freedom (DOF) articulated forceps. In order to design the required functionalities in EES, we consider a simplified surgical task scenario, with four basic instrument operations such as positioning, insertion, manipulation, and extraction. The proposed cooperative strategy is based on the combination of force based robot control for tool positioning, a virtual remote-center-of-motion (VRCM) during insertion/extraction tasks, and the use of a serial-link interface for precise and simultaneous control of the position and the orientation of the forceps tip. Virtual workspace constraints and motion scaling are added to provide safe and smooth control of our robotic surgical system. We evaluate the performance and usability of our system considering reachability, object manipulability, and surgical dexterity in an anatomically realistic human head phantom compared to the use of conventional surgical instruments. The results demonstrate that the proposed system can improve the precision, smoothness and safety of the forceps operation during an EES.

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