scholarly journals Hierarchical Shared Control of Cane-Type Walking-Aid Robot

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Chunjing Tao ◽  
Qingyang Yan ◽  
Yitong Li
Keyword(s):  
Potential Field ◽  
Control Method ◽  
Sensory System ◽  
Interaction Force ◽  
Human Motion ◽  
Shared Control ◽  
Artificial Potential Field ◽  
Intention Recognition ◽  
Walking Aid ◽  
Motion Intention

A hierarchical shared-control method of the walking-aid robot for both human motion intention recognition and the obstacle emergency-avoidance method based on artificial potential field (APF) is proposed in this paper. The human motion intention is obtained from the interaction force measurements of the sensory system composed of 4 force-sensing registers (FSR) and a torque sensor. Meanwhile, a laser-range finder (LRF) forward is applied to detect the obstacles and try to guide the operator based on the repulsion force calculated by artificial potential field. An obstacle emergency-avoidance method which comprises different control strategies is also assumed according to the different states of obstacles or emergency cases. To ensure the user’s safety, the hierarchical shared-control method combines the intention recognition method with the obstacle emergency-avoidance method based on the distance between the walking-aid robot and the obstacles. At last, experiments validate the effectiveness of the proposed hierarchical shared-control method.

scholarly journals sEMG Based Human Motion Intention Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Li Zhang ◽  
Geng Liu ◽  
Bing Han ◽  
Zhe Wang ◽  
Tong Zhang
Keyword(s):  
Human Motion ◽  
Human Machine Interaction ◽  
Existing Problems ◽  
Model Based ◽  
Wearable Robots ◽  
Intention Recognition ◽  
Future Challenges ◽  
Bioelectrical Signal ◽  
Machine Interaction ◽  
Motion Intention

Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

Study on Navigation of Wireless Sensor Network Mobile Robot

2012 ◽  
Vol 591-593 ◽  
pp. 1682-1686
Author(s):  
Cang Rong Zhao ◽  
Miao Miao Zheng
Keyword(s):  
Wireless Sensor Network ◽  
Mobile Robot ◽  
Real Time ◽  
Sensor Network ◽  
Potential Field ◽  
Control Method ◽  
Wireless Sensor ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Localization Algorithm

For optimal design of mobile robot path planning problem, an improved artificial potential field control method is designed. Using infrared sensors and ultrasonic sensors to detect the surrounding environment will get the information of obstacles and goals. Proposed an adaptive real-time localization algorithm based on improved DV-Hop algorithm to realize real-time localization for wireless sensor network mobile robot. The improved artificial potential field adopted the improved potential function that ensured the goal is the global minimum so the mobile robot can reach the goal freely. The effectiveness and feasibility of the improved algorithm verified by simulation.

scholarly journals A Novel Motion Intention Recognition Approach for Soft Exoskeleton via IMU

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2176
Author(s):  
Lu Zhu ◽  
Zhuo Wang ◽  
Zhigang Ning ◽  
Yu Zhang ◽  
Yida Liu ◽  
...  
Keyword(s):  
Inertial Sensor ◽  
Human Motion ◽  
Measurement Unit ◽  
Recognition Method ◽  
Metabolic System ◽  
Intention Recognition ◽  
Motion Modes ◽  
Deep Convolution Neural Network ◽  
Multi Mode ◽  
Motion Intention

To solve the complexity of the traditional motion intention recognition method using a multi-mode sensor signal and the lag of the recognition process, in this paper, an inertial sensor-based motion intention recognition method for a soft exoskeleton is proposed. Compared with traditional motion recognition, in addition to the classic five kinds of terrain, the recognition of transformed terrain is also added. In the mode acquisition, the sensors’ data in the thigh and calf in different motion modes are collected. After a series of data preprocessing, such as data filtering and normalization, the sliding window is used to enhance the data, so that each frame of inertial measurement unit (IMU) data keeps the last half of the previous frame’s historical information. Finally, we designed a deep convolution neural network which can learn to extract discriminant features from temporal gait period to classify different terrain. The experimental results show that the proposed method can recognize the pose of the soft exoskeleton in different terrain, including walking on flat ground, going up and downstairs, and up and down slopes. The recognition accuracy rate can reach 97.64%. In addition, the recognition delay of the conversion pattern, which is converted between the five modes, only accounts for 23.97% of a gait cycle. Finally, the oxygen consumption was measured by the wearable metabolic system (COSMED K5, The Metabolic Company, Rome, Italy), and compared with that without an identification method; the net metabolism was reduced by 5.79%. The method in this paper can greatly improve the control performance of the flexible lower extremity exoskeleton system and realize the natural and seamless state switching of the exoskeleton between multiple motion modes according to the human motion intention.

scholarly journals Human motion intention recognition based on EMG signal and angle signal

2021 ◽  
Vol 3 (1) ◽  
pp. 37-47
Author(s):  
Baixin Sun ◽  
Guang Cheng ◽  
Quanmin Dai ◽  
Tianlin Chen ◽  
Weifeng Liu ◽  
...  
Keyword(s):  
Human Motion ◽  
Emg Signal ◽  
Intention Recognition ◽  
Motion Intention

scholarly journals Formation control of UAVs based on artificial potential field

2018 ◽  
Vol 189 ◽  
pp. 10018
Author(s):  
Yongshen Lv ◽  
Xuerong Yang ◽  
Yajun Yang ◽  
Shengdong Pan ◽  
Chaojun Xin
Keyword(s):  
Kinetic Model ◽  
Potential Field ◽  
Formation Control ◽  
Control Algorithm ◽  
Simulation Experiment ◽  
Control Method ◽  
Artificial Potential Field ◽  
Field Function ◽  
Time Performance ◽  
Matching Rules

The problem of UAVs’ formation control in the process of motion is investigated in this paper. A formation control method based on artificial potential field of UAVs is proposed, established on the collision avoidance, aggregation and speed matching rules of UAVs. First establish the UAVs’ kinetic model in accordance to the motion rules, then design the formation control algorithm based on artificial potential field function, which is used to control the formation during the movement of UAVs. Finally, the results of simulation experiment show that the proposed formation control method in this paper is effective and has the advantages of easy realization, good real-time performance and excellent robustness.

Adaptive Robust Cascade Force Control of 1-DOF Joint Exoskeleton for Human Performance Augmentation

2015 ◽  
Author(s):  
Shan Chen ◽  
Bin Yao ◽  
Zheng Chen ◽  
Xiaocong Zhu ◽  
Shiqiang Zhu
Keyword(s):  
Force Control ◽  
Motion Tracking ◽  
Human Performance ◽  
Control Method ◽  
Interaction Force ◽  
Human Motion ◽  
Adaptive Robust Control ◽  
Human Machine Interaction ◽  
High Level ◽  
Machine Interaction

The control objective of exoskeleton for human performance augmentation is to minimize the human machine interaction force while carrying external loads and following human motion. This paper addresses the dynamics and the interaction force control of a 1-DOF hydraulically actuated joint exoskeleton. A spring with unknown stiffness is used to model the human-machine interface. A cascade force control method is adopted with high-level controller generating the reference position command while low level controller doing motion tracking. Adaptive robust control (ARC) algorithm is developed for both two controllers to deal with the effect of parametric uncertainties and uncertain nonlinearities of the system. The proposed adaptive robust cascade force controller can achieve small human-machine interaction force and good robust performance to model uncertainty which have been validated by experiment.

scholarly journals WIP Vehicle Control Method Based on Improved Artificial Potential Field Subject to Multi-obstacle Environment

2020 ◽  
Vol 49 (3) ◽  
pp. 320-334
Author(s):  
Ming Yue ◽  
Yigao Ning
Keyword(s):  
Potential Field ◽  
Nonholonomic Constraint ◽  
Control Method ◽  
Artificial Potential Field ◽  
Control Performance ◽  
Safe Distance ◽  
Security Issue ◽  
Repulsive Potential ◽  
The Stability ◽  
Key Parameter

This paper presents a control method for a WIP vehicle in multi-obstacle environment based on improved artificial potential field. Firstly, an improved artificial potential field (IAPF) is developed, where a safe distance is introduced to the existing repulsive potential field to solve the security issue, while the local minima can also be eliminated in the meantime. Next, an obstacle avoidance controller is designed based on the IAPF, where the nonholonomic constraint and underactuated characteristic of the WIP vehicle are fully considered, and the stability condition of the system is analyzed by means of the related control theory. Moreover, to further improve the control performance, a key parameter that play an important role in the controller is adjusted by taking advantage of fuzzy logic, and detailed analyses are given to demonstrate its necessity and effectiveness. Finally, considering a motion environment that contains dense obstacles, narrow corridor and an obstacle near the target, numerical simulations are conducted to validate the proposed method, whose results indicate that the method has a good performance to control the WIP vehicle in multi-obstacle environment.

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