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Page 116 Yang et al. Intell Robot 2024;4(1):107-24 I http://dx.doi.org/10.20517/ir.2024.07

3. EXPERIMENTS
In this section, simulation experiments are conducted with the constructed HEW simulation models in the
robot simulation platform CoppeliaSim (https://www.coppeliarobotics.com). The simulation model is shown
in Figure 5; the model retains the same configuration of degrees of freedom illustrated in Figure 3, where
the wheels are controlled with the PID position controllers and the support joint is controlled in the torque
mode to support the weight of the human-exoskeleton system. The torques and the trajectories of the COM’s
movement can be obtained, and the power and the energy cost of the HEW system can be calculated, with the
proposed CEEC approach; the optimal supporting force can be found after several walking steps. In addition
to this, the tracking performance of the COM’s movement can be used to evaluate the coordination between
the human-exoskeleton system and the robotic walker.

3.1. Experimental setup
To evaluate the performance of the proposed CEEC approach, several experiments were designed in the simu-
lation experiments. Firstly, the hip and knee joint angles used in the experiment are sampled from the healthy
subject, as shown in Figure 7, where the joint angles in swing and stance phases are for the swing leg and the
support leg, respectively. The mass distribution of the human-exoskeleton system (torso, thigh, shank, and
foot) follows average human anthropometry [27] , as shown in Table 1, and the length of the thigh and shank is
set to 0.45 m, which is similar to the subject with the body height of 1.75 m.

Overall, the total mass of the human subject, the exoskeleton, the robotic walker, and other parameters for the
simulation experiments are shown in Table 2. Note that the parameters for the motors in hip and knee joints
refer to the manual of the DC motors used in our exoskeleton robots shown in Figure 1.

To evaluate the proposed CEEC approach and compare it with others, four experiments were designed, each
assigned a distinct name.

• Thefirstoneisthe“baseline”; therewasnoactiveassistanceoftheroboticwalker,i.e.,thehuman-exoskeleton
system had to pull the robotic walker forward during walking.
• The second one is the Coordinated Motion Planning (“CMP”); there was only the active assistance from the
wheels with the generated coordinated motion planning, i.e., the wheels were controlled with the reference
joint angles generated in Section 2.2. In addition, there was no supporting force from the support joint.
• The third one is the “ESC”; there was only the supporting force from the support joint under the ESC
strategy, with no active assistance from the wheels.
• The last one is the “CEEC”; there was active assistance from both the support joint and wheels; the support-
ing force was optimized with the ESC strategy; the wheels were controlled with the reference joint angles
in Section 2.2.

To evaluate the adaption of the proposed CEEC approach for different subjects, three subject simulation mod-
els with various masses are employed in the experiment [Table 3]. Note that the ESC strategy is an online
iterative algorithm, and the initial value of the supporting force should be set at the beginning of the experi-
ment. Therefore, two distinct initial supporting forces were given [Table 3].

For each trial of the experiment, seventeen steps (the first step and eight gait cycles) were conducted to test
the efficiency of the proposed approach. Note that the first is a special step from the standing upright posture
to walking; therefore, the control strategy only works in the last sixteen steps. The sampling rate is 20 Hz, and
the gait cycle is two seconds with two steps. The TCoT was computed with the sampled torque of the support
leg’s hip and knee joints after each gait cycle. The parameters of ESC were selected as follows: a = 1.6, b = 0.8,
= 0.8 Hz, h = 0.4 Hz, = -6.