Design and Control of an Omni-Directional Robotic Walker Based on Human–Machine Interaction

Ji, Jiancheng; Chen, Wei; Wang, Wenbin; Xi, Jason

doi:10.1109/access.2021.3103202

Cited by 11 publications

(10 citation statements)

References 28 publications

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“…On the basis of previous studies [ 19 ], the novel robotic walker is designed to implement the pelvic trajectory control. Generally, a robotic walker consists of three parts: the OMP, the BWS and the PAM, as shown in the Fig.…”

Section: Methodsmentioning

confidence: 99%

Simulation study on assist-as-needed control of a rehabilitation robotic walker

Wang

Gong

Song

et al. 2023

THC

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BACKGROUND: Along with China entering an aging society, the percentage of people that over 60 will reach 34.9% in 2050, resulted in a significant increase in stroke patients. OBJECTIVE: This paper proposes a rehabilitation robotic walker for walking assistance during the daily life, and a control method for the motor relearning during the gait training. The walker consists of an omni-directional mobile platform (OMP) which ensures the walker can move on the ground, a body weight support system (BWS) which is capable of providing the desired unloading force, and a pelvic assist mechanism (PAM) to provide the user with four degrees of freedom and avoid the rigid impact. The study goal is to gain a better understanding of the assist-as-needed control strategy during the gait training. METHODS: For the man-machine interaction control, the assist-as-needed control strategy is adopted to guide the users’ motions and improve the interaction experience. To build the force field in the three-dimensional space, the dynamics of the system is derived to increase the accuracy of force control. RESULTS: The simulation results show that the force field around the motion trajectory was generated in the three-dimensional space. In order to understand the force field, we designed the simulation on sagittal plane and the controller can generate the appropriate force field. The preliminary experiment results were consistent with the simulation results. CONCLUSION: Based on the mathematical simulation and the preliminary test, the results demonstrate that the proposed system can provide the guide force around the target trajectory, the accuracy of force control still remains to be improved.

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Section: Methodsmentioning

confidence: 99%

Simulation study on assist-as-needed control of a rehabilitation robotic walker

Wang

Gong

Song

et al. 2023

THC

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“…According to robot kinematics and the earlier paper ( Ji et al, 2021 ), deducing the mapping relation

between the tracking velocity and angular velocity of the joints:

where

represents the set of the joint angle

, velocity

and accelerated velocity

, and

represents the set of the generalized position and posture vector

in the local coordinate system as defined in Eqs 2 , 3 :

…”

Section: Modeling and Controlmentioning

confidence: 99%

“…For the control levers, we can control the walker with two levers as described in the previous paper ( Ji et al, 2021 ). This paper addressed the prediction pattern with a single control lever, defining the lever vector as

, the magnitude of the vector as

, the direction of the vector as

, and the method to obtain the motion intentions is as follows:…”

Section: Modeling and Controlmentioning

confidence: 99%

Multimodal fusion and human-robot interaction control of an intelligent robot

Gong,

Chen,

Wang

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Small-scaled robotic walkers play an increasingly important role in Activity of Daily Living (ADL) assistance in the face of ever-increasing rehab requirements and existing equipment drawbacks. This paper proposes a Rehabilitation Robotic Walker (RRW) for walking assistance and body weight support (BWS) during gait rehabilitation.Methods: The walker provides the patients with weight offloading and guiding force to mimic a series of the physiotherapist’s (PT’s) movements, and creates a natural, comfortable, and safe environment. This system consists of an omnidirectional mobile platform, a BWS mechanism, and a pelvic brace to smooth the motions of the pelvis. To recognize the human intentions, four force sensors, two joysticks, and one depth-sensing camera were used to monitor the human-machine information, and a multimodal fusion algorithm for intention recognition was proposed to improve the accuracy. Then the system obtained the heading angle E, the pelvic pose F, and the motion vector H via the camera, the force sensors, and the joysticks respectively, classified the intentions with feature extraction and information fusion, and finally outputted the motor speed control through the robot’s kinematics.Results: To validate the validity of the algorithm above, a preliminary test with three volunteers was conducted to study the motion control. The results showed that the average error of the integral square error (ISE) was 2.90 and the minimum error was 1.96.Discussion: The results demonstrated the efficiency of the proposed method, and that the system is capable of providing walking assistance.

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“…During the picking process, the interacting force mainly consist of the positive pressure generated by the deformation of the flexible chuck and compression springs, the suction force generated by the evacuator of chuck, and the tensile force generated by the air cylinder and the guide screw. Then the generalized dynamic equation can be expressed as [18]:…”

Section: B Dynamics Of the Robotmentioning

confidence: 99%

Modeling and Force Analysis of a Harvesting Robot for Button Mushrooms

Yang

Cai

et al. 2022

IEEE Access

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With the improvement of the quality of life, the demands on button mushroom (Agaricus Bisporus) increase significantly. But traditional farming methods mainly rely on costly human picking, which is currently facing the time-consuming and labor-intensive problem. As machine vision technology becomes more mature, harvesting robot with flexible picking end-effector is an alternative method to address this issue. Though the conventional harvesting robot is capable of mushrooms picking, but the damage rate is relatively high for the improper picking force and motion. Thus, a novel picking end-effector for button mushrooms is designed based on vacuum negative pressure picking in this paper. The harvesting robot with flexible endeffector is proposed to solve the problem: 1) to avoid the injury when the end-effector touch the mushroom; 2) to increase the picking efficiency without damage rate increase. The structure of robotic mushroom picking end-effector is described in detail, and then the kinematic modeling and picking force analysis of this robot are presented. Lastly, the numerical simulation via the MATLAB is carried out to study the influence of the robot parameters. The bruise tests indicated that the maximal allowable stress is 0.196 MPa and mean allowable pressure is 13.82 N. Preliminary results demonstrate that the robot achieved 88.2% success rate and 2.9% damage rate in the factory environment, and there is potential for the automatic mushroom harvesting with the proposed harvesting robot.

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Design and Control of an Omni-Directional Robotic Walker Based on Human–Machine Interaction

Cited by 11 publications

References 28 publications

Simulation study on assist-as-needed control of a rehabilitation robotic walker

Simulation study on assist-as-needed control of a rehabilitation robotic walker

Multimodal fusion and human-robot interaction control of an intelligent robot

Modeling and Force Analysis of a Harvesting Robot for Button Mushrooms

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