**Abstract**

In this chapter, the authors will propose the active gait generation of a quadruped robot. We developed the quadruped robot system using self-inhibited pulse-type hardware neuron models (P-HNMs) as a solution to elucidate the gait generation method. We feedbacked pressures at the robot system's each foot to P-HNM and varied the joints' angular velocity individually. We experimented with making the robot walk from an upright position on a flat floor. As a result of the experiment, we confirmed that the robot system spontaneously generates walk gait and trot gait according to the moving speed. Also, we clarified the process by which the robot actively generates gaits from the upright state. These results suggest that animals may generate gait using a similarly simple method because P-HNM mimics biological neurons' function. Furthermore, it shows that our robot system can generate gaits adaptively and quite easily.

**Keywords:** gait generation, interlimb coordination, pulse-type hardware neuron model (P-HNM), locomotion, quadruped robot, gait pattern

#### **1. Introduction**

Improvements in computer processing capability have realized advanced mobile robots [1, 2]. However, there is still no legged robot that can move flexibly enough to change our lives in a big way. One of the reasons for this is challenging to act autonomously for current control methods to instantly adapt to various events occurring in the robot's surroundings. The realization of the autonomous robot needs a higher sensory information processing system. Also, increasing the number of sensors or shorten the interval acquisition of the sensor information requires high-speed information processing. On the other hand, animals can easily act autonomously. The significant difference between robots and animals in deciding how they should act is to process all information by their brains or not. Mimicking the animal's biological system can be useful for realizing simple robot control [3, 4]. For example, the typical actions that animals unconsciously generate are respiration, chewing, and walking [5, 6]. The elucidation of generating walking action may solve the problem of current control methods for legged robots. The ordinary quadrupeds as legged animals have several locomotion patterns (gaits) [7–9]. Neurophysiology experiments have provided many insights into the characteristics

and kinematics in gait [10–18]. The finding that horses move efficiently by switching their gait to suit the situation is essential for the engineering application of their gait generation mechanisms [19]. Researchers also examined how quadrupeds generate gait [19–22]. The theory that quadruped animals unconsciously generate gaits by the interaction between the central pattern generator (CPG) and sensory inputs is widely accepted [23–26]. The variety of animals' functions makes it difficult to use their bodies to identify the essential elements required to generate gait. Although there is much discussion on the animal's gait generating mechanisms, most of it is still unclear [27, 28].

Researchers have attempted to realize CPG in engineering and use modeled CPGs to control robots [29–35]. These studies have succeeded in using the CPG models to generate locomotion, which was previously calculated by the processor [32–35]. However, how does an animal's CPGs create the gait according to the robot's surroundings is unclear. It is necessary to examine a method for generating gait employing body structure to identify the essential elements required for entities to generate gait.

Research using a biped machine with passive joints revealed that the biped machine generates a gait pattern without a control mechanism when placed on a shallow slope [36]. Another research using a quadruped machine revealed that it generates quadruped animal's gaits and switch them according to the body joints' type and the slope angle [37]. Furthermore, even if the legs' number increased to six or more, a machine generates gaits [38]. These experiments suggest that even machines without a control mechanism can generate gaits by using gravity. The finding that walking machines produce different gait depending on body structure may be related to the fact that animals have different gait for different species. Realizing a robot that can actively walk requires studying the gait generation mechanism, including the actuator's driving method.

Recently, a quadruped robot system with joints using servomotors controlled by decoupled mathematical oscillators based on the active rotator model has been proposed [39–41]. The quadruped robot's legs are controlled according to the oscillator's phase individually. Feeding back each foot's pressure to the oscillators to accelerate and decelerate the joint's angular velocity has generated the phase difference (i.e., gait). The quadruped robot could generate an animal's gait according to the pressures. Another robot controlled in the same method could switch the gaits according to its moving speed [42]. These results suggest the effectiveness of using the difference in pressure on each foot to generate gaits. Although they did not design the oscillator they used to control the joint on a biological basis, the suggestion that the reaction force the leg receives from the floor is closely related to the gait is consistent with the results of physiological experiments [16, 17].

The authors speculate that spike firing has significant roles in information processing in the nervous system. We are studying robot control using pulse-type hardware neuron models (P-HNMs) that can output the spike firing (action potential) the same as a biological neuron [43]. Our research aims to develop a simple and efficient control method for robots using the artificial motor nervous system and central nervous systems. Hardware implementation will be advantageous in a large scale network system. The authors developed a quadruped robot system that implemented an active gait generation mechanism using P-HNMs. The mechanism is similar to the peripheral nervous system in that independent P-HNMs control each limb individually [44].

This chapter describes the active gait generation method for a quadruped robot. Firstly, the authors introduce the components of our quadruped robot system. Secondly, we will discuss the gait generation method, and finally, we will show the experimental result.

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**Figure 1.**

*Quadruped robot system.*

*Active Gaits Generation of Quadruped Robot Using Pulse-Type Hardware Neuron Models*

**Figure 1** shows our constructed quadruped robot system. This section describes

The mechanical components of the robot system consist of the body frame and four-leg units. **Figure 2** shows the structure of the robot's body. We machined Part 1, 2, 3, and 4 from aluminum alloy sheets using the computerized numerical control (CNC) machining system and bender. Also, we formed Part A and B using a 3D printer. Part 2 and Part 3 were jointed together by Part 1 to form the body frame to connect the legs (**Figure 3**). The leg units consist of Part A, B, and two Part 4 and servomotors KRS-2552 (Kondo Kagaku co., ltd, available online at https:// kondo-robot.com/ [45]). All the leg units have the same structure. The leg length is 138 mm (from joint axis to toe), the distance between the front and rear legs is 175 mm, and the distance between the left and right legs is 101 mm. The total weight of the robot system is approximately 1.1 kg. We gave the robot system two joints for each leg, the minimum number needed to walk. This robot system has no degrees of

The robot system's electrical components consist of self-inhibited P-HNM circuit boards, pressure sensors FSR402 (Interlink Electronics, Inc., available online at https://www.interlinkelectronics.com/ [46]), a single-board microcontroller Arduino DUE, and a peripheral circuit board. The pressure sensors have attached to the feet shown in **Figure 4**. Also, we mounted a battery and Bluetooth module to experiment without physical connections for power supply and data logging. The

The self-inhibited P-HNM consists of a cell body model and an inhibitory synaptic model. **Figure 5** shows the schematic and circuit diagram of the self-inhibited P-HNM. **Figure 5(a)** shows the connection between the cell body model and the inhibitory neuron model. A large circle represents the cell body model, and a black

self-inhibited P-HNM and the peripheral circuit board are described later.

*DOI: http://dx.doi.org/10.5772/intechopen.95760*

**2.1 Mechanical and electrical components**

the individual components of the quadruped robot system.

**2. Quadruped robot system**

freedom except for legs.

**2.2 Self-inhibited P-HNM**

*Active Gaits Generation of Quadruped Robot Using Pulse-Type Hardware Neuron Models DOI: http://dx.doi.org/10.5772/intechopen.95760*
