4.1. Locomotion of the quadruped robot

Figure 14 shows the discrete circuits of the hardware ANNs. The electrical components were mounted on a frame-retardant type 4 (FR4) circuit board (The circuit diagram is shown in Figures 6 and 9.) The hardware ANN consisted of four sets of an excitatory-inhibitory neuron

Figure 14. The hardware ANNs are constructed as discrete circuits.

pair model connected as shown in Figure 10a. The width and length of the mounted hardware ANNs are 100 and 80 mm, respectively; therefore, the hardware ANNs are sufficiently small to install on the quadruped robot.

Figure 13 shows the inhibitory mutual coupling of the pulse-type hardware neuron models subjected to a single external trigger pulse. (a) The connection diagram of this system, and (b) a typical output waveform under the sequence vext1, vext2, vext3, and vext<sup>4</sup> of the trigger pulse (the forward walk sequence in Figure 5). Before applying the external trigger pulse, the output sequence was vI1, vI2, vI4, and vI3. After applying the pulse, it was corrected to vI1, vI2, vI3, and vI4. Therefore, the single-pulse correction realizes the forward and backward locomotion patterns in Figure 5. Note that walking and galloping in Figure 2 and forward and backward locomotion patterns in Figure 5 are all realized by the four-phase alternating oscillation and differ only in the order of their output sequences. Figure 13c shows a typical output waveform when the sequence of the external trigger pulse is vext1, vext2 and (simultaneously) Vext3, Vext4 (the bound sequence in Figure 2). The bound sequence is not realized by the external trigger pulse. The inhibitory mutual coupling of the pulse-type hardware neuron model cannot by itself generate the locomotion patterns of trot, pace, and bound because these motions are two-phase alternating oscillations.

In this section, the gait rhythms generated by the hardware ANNs are tested in a multilegged robot.

Figure 14 shows the discrete circuits of the hardware ANNs. The electrical components were mounted on a frame-retardant type 4 (FR4) circuit board (The circuit diagram is shown in Figures 6 and 9.) The hardware ANN consisted of four sets of an excitatory-inhibitory neuron

4. Results and discussion

42 Advanced Applications for Artificial Neural Networks

4.1. Locomotion of the quadruped robot

Figure 14. The hardware ANNs are constructed as discrete circuits.

Figure 15 shows the quadruped robot mounted with the hardware ANN circuit board. The quadruped robot system is 130 mm wide, 140 mm long, 100 mm high, and 530 g in weight. The power consumption of the hardware ANNs was approximately 360 mWh.

Walk sequence is the basic motion of the quadruped robot. Figure 16 shows the generated gait pattern and leg motion of a quadruped robot. Panels (a) and (b) show the driving rhythm of the (measured) walking gait pattern and the leg motion of the robot, respectively. Under the

Figure 15. Quadruped robot system mounted with the hardware ANNs as shown in Figure 14.

Figure 16. Generated gait pattern and leg motion of a quadruped robot (walk sequence). (a) Waveform and (b) leg motion.

waveform shown in Figure 16a, the legs move as shown in Figure 16b. In other words, the generated driving rhythm is a four-phase alternating oscillation with the sequence left foreleg (LF), right hindleg (RH), right foreleg (RF), and left hindleg (LH).

The locomotion of a walking quadruped robot driven by the hardware ANNs is captured in Figure 17. The motion patterns resemble those of a quadruped animal, thus confirming that the driving rhythms generated by the hardware ANNs can realize proper walking behavior. Moreover, the hardware ANNs can generate various oscillatory patterns without requiring computer programs.

Under an external trigger pulse, the constructed hardware ANNs can change the gait pattern of the quadruped robot. A walk-to-trot gait change is illustrated in Figure 18. The external trigger pulse is generated by a waveform generator applied to the input port (see Figure 10). Considering that the hardware ANNs can memorize the applied gait rhythm, the quadruped robot can switch its locomotion pattern by applying an external input to its hardware ANNs.

### 4.2. Locomotion of the hexapod robot

Figure 19 shows the IC of the hardware ANNs. Panel (a) shows the layout pattern of the bare IC chip of the hardware ANNs. The design rule of the bare IC chip is four-metal two-poly CMOS (0.35 <sup>μ</sup>m). The chip is sized (2.45 2.45) mm<sup>2</sup> . The hardware ANNs are connected as

Figure 17. Locomotion (walk) of the quadruped robot driven by hardware ANNs.

Gait Generation of Multilegged Robots by using Hardware Artificial Neural Networks http://dx.doi.org/10.5772/intechopen.70693 45

Figure 18. Example of changing the gait pattern from walk to trot. (a) Waveform and (b) leg motion.

waveform shown in Figure 16a, the legs move as shown in Figure 16b. In other words, the generated driving rhythm is a four-phase alternating oscillation with the sequence left foreleg

The locomotion of a walking quadruped robot driven by the hardware ANNs is captured in Figure 17. The motion patterns resemble those of a quadruped animal, thus confirming that the driving rhythms generated by the hardware ANNs can realize proper walking behavior. Moreover, the hardware ANNs can generate various oscillatory patterns without requiring

Under an external trigger pulse, the constructed hardware ANNs can change the gait pattern of the quadruped robot. A walk-to-trot gait change is illustrated in Figure 18. The external trigger pulse is generated by a waveform generator applied to the input port (see Figure 10). Considering that the hardware ANNs can memorize the applied gait rhythm, the quadruped robot can switch its locomotion pattern by applying an external input to its hardware ANNs.

Figure 19 shows the IC of the hardware ANNs. Panel (a) shows the layout pattern of the bare IC chip of the hardware ANNs. The design rule of the bare IC chip is four-metal two-poly

. The hardware ANNs are connected as

(LF), right hindleg (RH), right foreleg (RF), and left hindleg (LH).

computer programs.

4.2. Locomotion of the hexapod robot

44 Advanced Applications for Artificial Neural Networks

CMOS (0.35 <sup>μ</sup>m). The chip is sized (2.45 2.45) mm<sup>2</sup>

Figure 17. Locomotion (walk) of the quadruped robot driven by hardware ANNs.

Figure 19. The hardware ANNs are constructed as IC. (a) Layout design and (b) bare IC chip with FR4 circuit board.

shown in Figure 13. Four cell body models are mutually coupled by 12 inhibitory synaptic models. The driving waveform of the hexapod robot is generated by the outputs extracted from the hardware ANNs and the current mirror circuit. Four trigger pulse input ports are also extracted from the hardware ANNs. The sequence of the locomotion rhythm depends on the

Figure 20. Measured output waveforms of the designed IC.

Figure 21. Locomotion (walking) of the hexapod robot mounted with the hardware ANNs.

timing of the single external trigger pulse. Figure 19b shows the constructed bare IC chip, which is fixed to the cavity of an FR4 circuit board by wire bonding.

Figure 20 shows the measured output waveform of the designed IC. The hardware ANNs can generate the locomotion rhythms observed in living organisms. To sufficiently heat and cool the artificial muscle wires, the pulse width, period, and amplitude were set to 0.5 s, 2 s, and 75 mA, respectively. The connected helical artificial muscle wires are approximately 50 Ω. As shown in Figure 20, the output waveform effectively actuates the actuator of the hexapod robot. The approximate power consumptions of the hardware ANNs and the current mirror circuit were 0.708 and 488 mWh, respectively. The former almost matches the power consumption of biological neural networks, but the power consumption of the artificial muscle wire was excessive.

The circuit in Figure 19b was mounted on the hexapod robot. Figure 21 shows snapshots of the walking hexapod robot system. The driving waveforms generated by the hardware ANNs actuate the hexapod robot, thus enabling successful locomotion.
