Pneumatic Artificial Muscles (PAMs) are actuators that resemble human muscles, and offer an attractive performance in various aspects including robustness, simplicity, high specific power and high force for a given volume. These characteristics render them good candidates for use in humanoid robots. The use of traditional PAMs to closely mimic human structures, however, is difficult due to their relatively large size and relatively fixed designs. The recent development of multifilament PAMs enables the realization of humanoid robots that more closely mimic the human anatomy. In this paper, the application of multifilament PAMs to mimic the human neck is presented. First, the main structures of the human neck anatomy in terms of bones, ligaments and muscles are identified and detailed. The design to mimic each of these structures is subsequently described, together with the most relevant parts of the manufacturing process. The integrated neck is then presented, and the method to actuate it is outlined. The results of motion of the artificial neck when actuating different groups of muscles that mimic those in the human anatomy are reported, confirming a motion that is equivalent to that of the human neck. The results also indicate a range of motion of the robot neck somewhat lower than that of its human counterpart, and the reasons for this are discussed. Finally, future directions for improved motion range, stability, durability and efficiency are outlined.
Casting manipulation has been studied to expand the robot's movable range. In this manipulation, the robot throws and reaches the end effector to a distant target. Usually, a special casting manipulator, which consists of rigid arm links and specific flexible linear objects, is constructed for an effective casting manipulation. However, the special manipulator cannot perform normal manipulations, such as picking and placing, grasping, and operating objects. We propose that the normal robot arm, which can perform normal tasks, picks up an unknown string in the surrounding environment and realizes casting manipulation with it. As the properties of the string are not provided in advance, it is crucial how to reflect it in casting manipulation. This is realized by the motion generation of the robot arm with the simulation of string movement, actual string manipulation by the robot arm, and string parameter estimation from the actual string movement. After repeating these three steps, the simulated string movement approximates the actual to realize casting manipulation with the unknown string. We confirmed the effectiveness of the proposed method through experiments.
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