Dynamic Balance Gait Planning for Lower Limb Exoskeleton Robot using COP Criterion and IMU Sensor

Habib Mohamad^*(1), Sadjaad Ozgoli⁽¹⁾, Fadi Motawej⁽²⁾, Ghada Saad⁽³⁾   

1-    Faculty of Electrical and Computer Engineering, Control Department, Tarbiat Modares University, Tehran, Iran

2-    Faculty of Engineering, Mechatronics Department,  Manara University, Lattakia, Syria

3-    Faculty of Mechanical and Electrical Engineering, Biomedical Department,  Tishreen University, Lattakia, Syria

H_mohamad@modares.ac.ir

Abstract

In recent years, lower limb exoskeleton robots have received much attention due to their potential to help people with paraplegia walk again. Crutches or walking aids have been used to maintain balance until now.  Using these walking aids will cause the patient fatigue over time especially when much weight is put on them during walking. So, an essential subject in controlling these robots is gait planning and saving balance as possible, which there are still challenges. Human gait locomotion is described in three primary planes of the human body: frontal, transversal, and sagittal. The balance in the sagittal plane can be achieved by generating the trajectory for the center of mass of the human-exoskeleton system using the center of pressure criterion. On the other hand, the balance issue in the frontal plane is studied by calculating the required time for preventing lateral falling. The performance analysis of the proposed approach is evaluated via a non-disabled human wearing the Exoped^® exoskeleton, where he can walk without crutches.

Keywords: Lower Limb Exoskeleton, Center of Pressure, Dynamic Balance.

1.     Introduction

Lower Limb Exoskeletons (LLEs) have been interesting in recent years due to the increasing number of paraplegic patients [1]. Much progress has been made in this field, especially in manufacturing and control [2]. The control schematic has been implemented in three stages [3]. The first stage determines the required gait parameters based on human intention and exoskeleton movement. The second stage is the trajectory planner of the joints and is responsible for providing the joint reference angles to the third stage. Finally, in the third stage, a common controller PID will be used to track the joint reference angles.

Many approaches have been proposed for the second stage. Joint trajectories were recorded by a motion capture system with many infrared cameras and reflective markers attached to a non-disabled subject or during the subject’s walking by the robot in zero-torque mode [4]. The hip position trajectory was designed by three continuous differentiable polynomial segments, and the ankle position trajectory was modeled based on sinusoidal functions [5]. A real-time walking pattern with changeable gait parameters was presented where minimum-jerk trajectories were planned using a feedback third-order system for hip and ankle positions in the task space [6]. An online gait generator, according to the gait parameters, is proposed in [7], where the developed generator is suitable for level ground, slopes, stairs, and obstacle avoidance. A minimum-time and minimum-jerk gait planner in the joint space is introduced in [8], where a third-order system for the joints is defined, and the cost function is introduced to minimize the jerk of the joints throughout the stride. It should be emphasized that none of the previous methods discussed the balance issue for the human-exoskeleton system seriously.

Balance is the most critical concern when the trajectory is generated. Human gait locomotion can be described in three primary planes of the human body: frontal, transversal, and sagittal [9], as shown in Fig. 1. The balance issue has been studied in the sagittal plane because the motors of the robots rotate in this plane only, and crutches must be used to maintain balance. The trajectory of the hip position was generated using the linear inverted pendulum model to ensure the backward balance of the robot by introducing an initial velocity to the center of mass (COM) of the human-exoskeleton system [10]. Forward balance can be saved using walking aids. Using crutches and maintaining balance over time will cause arm fatigue. Therefore, the center of pressure (COP) of the patient-exoskeleton system with crutches should be close to support feet, and not much weight should be carried on crutches [11]. Therefore, in the sagittal plane,  a trajectory for COM will be generated in this paper to maintain a backward balance and reduce forces applied to the arms. In addition, the required time to save the lateral balance in the frontal plane will be calculated, and the robot step will be completed before the fall occurs.

The remainder of this paper is organized as follows: methods and materials are presented in section 2. In section 3, the dynamic balance gait planner will be explained. Then, experimental results are presented in section 4. Finally, the paper is concluded in section 5.

 

 

 

Fig. 1 Primary planes of the human body.

2.     Methods and Materials

2.1.                      Exoped^® Robot

Exoped^® robot, as shown in Fig. 2, is designed by Pedasys Co. (Iran) to help complete lower limb paraplegic patients walk again. The lengths of the shank and thigh are adjustable to suit the different heights of the patients. Also, hip-width is adjustable to meet different sizes of them. It has six degrees of freedom (DoF) in the sagittal plane: ankle, knee, and hip joints for both the left and right legs. The knee and hip joints of each leg are powered by brushless DC flat motors. In contrast, the ankle joints are passive and equipped with a spring to reduce the impact and shock when the exoskeleton contacts the ground. A pair of crutches is used to maintain balance. The braces and straps at the waist, thighs, shanks, and feet are designed to attach the patient to the exoskeleton, transmitting auxiliary torque to the patient.

 

 

Fig. 2 Lower limb exoskeleton robot, Exoped^®.

The gait of the robot is divided into two phases: double stance phase and single stance phase. In the double stance phase, the balance issue is accomplished by two feet. On the other hand, the balance issue still has challenges in the single support phase where much weight is put on the crutches which causes the patient fatigue.   The gait parameters used in this paper are: the double support phase duration , the single support phase duration , the step length , step height . ,  and  are assumed to be constants, whereas  will be calculated to achieve balance in the frontal plane where the step must be completed before the lateral fall occurs.

2.2.                      Orientation Estimation

 

 

Fig. 3 Inertia measurement unit IMU, MPU 6050.

 

 

 

3.     Dynamic Balance Gait

Human gait is divided into two phases: double support phase and single support phase. In this paper, we focus to generate the trajectory of the support leg in the single support phase that is responsible for the balance of the human-exoskeleton system. While the trajectory of all joints in the double support phase and the swing leg in the single support phase will be generated with a minimum jerk as described in [8]. Most researchers use the inverted pendulum to model human gait. So obtaining the dynamic model of the inverted pendulum is the first step to maintaining human balance during walking. The nature of the inverted pendulum is unstable, but it has a stable point at the vertical position. Dynamic balance is related to both position and velocity of the COM of the pendulum.

 

 

 

 

Fig. 4 Inverted pendulum.

 

 

 

Fig. 5 Human-exoskeleton system and its inverted pendulum model in the sagittal plane (x-direction).

 

 

 

Fig. 6 Human-exoskeleton system and its inverted pendulum model in the frontal plane (z-direction)

 

 

 

4.     Experimental Results

 

Fig. 7 Simulation results of the dynamic balance trajectory generator in the sagittal plane.

 

Fig. 8 Simulation results of the trajectory of COM in the frontal plane.

 

 

Fig. 9 Experimental test of the proposed approach.