This chapter explains how the MEMS microrobot system could perform the walking behavior of ants. MEMS microrobot system consists of micro-mechanical systems and micro-electro systems. The micro-mechanical systems mimic the alternating tripod gait of an ant by the shape memory alloy-type rotary actuator and the link mechanism. The micro-electro systems mimic the electrical activity of biological neural networks using the artificial neural networks IC. The artificial neural networks IC generates the driving pulses of shape memory alloy-type rotary actuator without using software programs. The micro-mechanical systems and micro-electro systems are integrated as a robot system. As a result, the authors show that the MEMS microrobot system could perform the ant-like walking behavior with a speed of 20 mm/min. The MEMS microrobot system was 0.079 g in weight, 4 mm width, 4 mm length, and 5 mm height in size. The robot system needs only the electrical power source as an external device.
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Biomimetics has been studied in many research fields because organisms have useful functions to develop technology (e.g., Habib, 2011; Seok et al., 2015). In particular, the locomotion mechanisms of insects attract the attention of researchers to develop microrobots. Hoover et. al. (2008) proposed the crawling hexapod robot RoACH, and Wood et. al. (2012) proposed the flying microrobot RoboBee. Mimicking the locomotion mechanisms of insects is useful to realize small sized actuators for millimeter scale microrobots. If the microrobot system could perform such as insects, microrobots can be used for agriculture, manufacturing, medicine, informational technology and so on. However, further miniaturization of the microrobot is required. In the further miniaturizations, some researchers use micro fabrication technology to fabricate the small sized actuators of the microrobot (e.g., Donald, Levey, McGray, Paprotny, & Rus, 2006; Edqvist, Snis, Mohr, Scholz, Corradi, Gao, ... Johansson, 2009). The development of the small sized actuator was categorized into some groups (e.g., Fearing, 1998). For example, electric field driven actuators, magnetic field driven actuators, piezoelectric driven actuators, shape memory alloy driven actuators, electrostatic driven actuators, ion-exchange polymer driven actuators, and so on (e.g., Tang, Nguyen, & Howe, 1989; Sniegowski, & Garcia, 1996; Asada, Matsuki, Minami, & Esashi, 1994; Suzuki, Tani, & Sakuhara, 1999; Surbled, Clerc, Pioufle, Ataka, & Fujita, 2001; Yeh, Hollar, & Pister, 2002; Bell, Lu, Fleck, & Spearing, 2005; Ryu, Jeong, Tak, Kim, Kim, & Park 2002). The actuators using field forces are advantageous for miniaturization because the power source and the controller of the actuator do not need to be implemented on the robot body. However, the robot would not be able to locomote without the external controller. Thus, the actuators have to generate the driving force by themselves and be integrated inside the robot body to locomote the robot independently. The researchers have to choose the actuators because each actuator has different advantages, such as power consumption, switching speed, force generation, displacement, fabrication difficulty, and so on. Also, actuators can only generate rotary motion or linear motion; therefore, mechanical design is necessary to create microrobots that locomote using a step pattern.
Besides further miniaturization, microrobot systems require higher functionalization. Insects can control the movement according to the environmental situation because the brain can process the sensory information. A small size autonomous intelligent system realized by the brain can be a good example for the microrobot system. However, it is difficult to program the autonomous intelligent system on a small microcontroller. Some advanced studies have been done for applying artificial neural networks to robot control. Many studies have reported on both software models and hardware models (e.g., Matsuoka, 1987; Ikemoto, Nagashino, kinouchi, & Yoshinaga, 1997; Nakada, Asai, & Amemiya, 2003). The software model is useful because it is easy to program on a microcontroller. The program could change or update under different conditions. In contrast, using the hardware model is difficult to reconstruct the structure of the circuit. The insects use small sized biological neural networks which process information by oscillatory patterns. Hardware models which can generate an oscillatory pattern such as a biological neuron have possibilities to construct a new information processing method in a simple and compact system.