29
CPG-Based Control of Serpentine Locomotion of a Snake-Like Robot
(a) N = 1
(b) N = 1
FIGURE 2.14
Movement with different numbers of S-shapes: (a) motion trajectory with one S-shape and (b)
motion trajectory with two S-shapes.
of our ten-link snake-like robot is limited to two. Moreover, the method for
changing motion speed by adjusting locomotion frequency was verified
by experiment. Though the snake-like robot keeps the same curvature and
S-shape as shown in Figure 2.13b, the motion velocity of the robot becomes
0.0839 and 0.1694 m/s with respect to the different time constant of the CPG
(τ 1 , τ 2 ) of (4.0, 12.0) and (2.0, 6.0). Because the robot will slip more frequently
with the increase of speed of motion, the frequency of locomotion cannot be
set too high.
By implementing the proposed control method in Sections 2.4.4 and 2.4.5,
the snake-like robot can exhibit a changed locomotion for obstacle avoidance. As shown in Figure 2.15, when the robot encounters an obstacle, a
command of turn motion can be sent to the CPG controller by the wireless
module, and then an asymmetric locomotion is perform to avoid the obstacle. A round motion of the snake-like robot is also conducted experimentally
as shown in Figure 2.16. In the experiments, a continuous locomotion curve
of the snake-like robot was performed during the transmission of angle signals from the head joint to the tail joint.
2.6 Summary
In this chapter, a bio-inspired system imitating the CPG neural network has
been proposed as the control method for a snake-like robot. The CPG network with a feedback connection does not necessitate additional adjustments
CPG-Based Control of Serpentine Locomotion of a Snake-Like Robot
(a) N = 1
(b) N = 1
FIGURE 2.14
Movement with different numbers of S-shapes: (a) motion trajectory with one S-shape and (b)
motion trajectory with two S-shapes.
of our ten-link snake-like robot is limited to two. Moreover, the method for
changing motion speed by adjusting locomotion frequency was verified
by experiment. Though the snake-like robot keeps the same curvature and
S-shape as shown in Figure 2.13b, the motion velocity of the robot becomes
0.0839 and 0.1694 m/s with respect to the different time constant of the CPG
(τ 1 , τ 2 ) of (4.0, 12.0) and (2.0, 6.0). Because the robot will slip more frequently
with the increase of speed of motion, the frequency of locomotion cannot be
set too high.
By implementing the proposed control method in Sections 2.4.4 and 2.4.5,
the snake-like robot can exhibit a changed locomotion for obstacle avoidance. As shown in Figure 2.15, when the robot encounters an obstacle, a
command of turn motion can be sent to the CPG controller by the wireless
module, and then an asymmetric locomotion is perform to avoid the obstacle. A round motion of the snake-like robot is also conducted experimentally
as shown in Figure 2.16. In the experiments, a continuous locomotion curve
of the snake-like robot was performed during the transmission of angle signals from the head joint to the tail joint.
2.6 Summary
In this chapter, a bio-inspired system imitating the CPG neural network has
been proposed as the control method for a snake-like robot. The CPG network with a feedback connection does not necessitate additional adjustments