Kinematic Analysis and Motion Planning of Cable-Driven Rehabilitation Robots

In this study, a new cable-driven rehabilitation robot is designed, the overall design of the robot is given, and the kinematic equation of the lower limbs in the supine state of the human body is addressed. Considering that cable winders move along the rail brackets, the closed vector method is applied to establish the kinematic model of the robot, and the relationship between the human joint angle and the cable length change was deduced. Considering joint compliance, a fifth-order polynomial trajectory planning method based on an S-shaped curve is proposed by introducing an S-shaped velocity curve, and the changes in cable length displacement, velocity, and acceleration are simulated and analyzed. Three planning methods are compared based on two indices, and experimental verification is carried out on the rehabilitation experiment platform. The simulation and experimental results show that the trajectory planning method presents low energy consumption and strong flexibility, and can achieve better rehabilitation effect, which builds a good basis for the subsequent study of dynamics and control strategy.

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Robot arm trajectory planning study for a table tennis robot

E3S Web of Conferences ◽

10.1051/e3sconf/202126003009 ◽

2021 ◽

Vol 260 ◽

pp. 03009

Author(s):

Bo Zhang ◽

Bingqiang Chen ◽

Yansong Deng

Keyword(s):

Success Rate ◽

Trajectory Planning ◽

Kinematic Model ◽

Joint Space ◽

Degree Of Freedom ◽

Planning Method ◽

Robot Arm ◽

Table Tennis ◽

Planning Study ◽

Physical Tests

The 4 degree of freedom robot arm of a table tennis robot has a variety of trajectories. In order to improve the response and the success rate of the shots, we used the joint space trajectory planning method to establish a kinematic model with the robot arm joints as variables, and by combining it with the robot arm kinematics, we obtained the relevant parameters for each joint of the robot arm. Simulation tests and physical tests were carried out to obtain a more accurate trajectory of the robot arm.

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Cosine Second Order Robot Trajectory Planning Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.80-81.1075 ◽

2011 ◽

Vol 80-81 ◽

pp. 1075-1080

Author(s):

Zong Wu Xie ◽

Cao Li ◽

Hong Liu

Keyword(s):

Path Planning ◽

Trajectory Planning ◽

Trajectory Tracking ◽

Joint Space ◽

Planning Method ◽

Robot Trajectory ◽

Great Performance ◽

Planning Methods ◽

Planning Algorithm ◽

Path Planning Algorithm

A new joint space trajectory planning method for the series robot is proposed. Comparing with the traditional path planning methods which can only guarantee the planned trajectory velocity or acceleration continuous, the proposed trajectory planning algorithm can also ensure the derivative of acceleration (Jerk) continuous within a limit threshold. At the end of this paper, the proposed path planning algorithm is validated of having a great performance on robot trajectory tracking.

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A Feedrate Planning Method for the NURBS Curve in CNC Machining Based on the Critical Constraint Curve

Applied Sciences ◽

10.3390/app11114959 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4959

Author(s):

Peng Guo ◽

Yijie Wu ◽

Guang Yang ◽

Zhebin Shen ◽

Haorong Zhang ◽

...

Keyword(s):

Planning Method ◽

Cnc Machining ◽

Arc Length ◽

Kinematic Constraint ◽

Nurbs Curve ◽

Planning Strategy ◽

Feedrate Planning ◽

Curvature Constraint ◽

Planning Methods ◽

The Relationship

The curvature of the NURBS curve varies along its trajectory, therefore, the commonly used feedrate-planning method, which based on the acceleration/deceleration (Acc/Dec) model, is difficult to be directly applied in CNC machining of a NURBS curve. To address this problem, a feedrate-planning method based on the critical constraint curve of the feedrate (CCC) is proposed. Firstly, the problems of existing feedrate-planning methods and their causes are analyzed. Secondly, by considering both the curvature constraint and the kinematic constraint during the Acc/Dec process, the concept of CCC which represents the relationship between the critical feedrate-constraint value and the arc length is proposed. Then the CCC of a NURBS curve is constructed, and it has a concise expression conforming to the Acc/Dec model. Finally, a feedrate-planning method of a NURBS curve based on CCC and the Acc/Dec model is established. In the simulation, a comparison between the proposed method and the conventional feedrate-planning method is performed, and the results show that, the proposed method can reduce the Acc/Dec time by over 40%, while little computational burden being added. The machining experimental results validate the real-time performance and stability of the proposed method, and also the machining quality is verified. The proposed method offers an effective feedrate-planning strategy for a NURBS curve in CNC machining.

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A Time Optimal Trajectory Planning Method for Double-Pendulum Crane Systems with Obstacle Avoidance

IEEE Access ◽

10.1109/access.2021.3050258 ◽

2021 ◽

pp. 1-1

Author(s):

Wa Zhang ◽

He Chen ◽

Haiyong Chen ◽

WeiPeng Liu

Keyword(s):

Obstacle Avoidance ◽

Trajectory Planning ◽

Optimal Trajectory ◽

Planning Method ◽

Double Pendulum ◽

Time Optimal ◽

Optimal Trajectory Planning

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An Estimator for the Kinematic Behaviour of a Mobile Robot Subject to Large Lateral Slip

Applied Sciences ◽

10.3390/app11041594 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1594 ◽

Cited By ~ 1

Author(s):

Andrea Botta ◽

Paride Cavallone ◽

Luigi Tagliavini ◽

Luca Carbonari ◽

Carmen Visconte ◽

...

Keyword(s):

Experimental Data ◽

Mobile Robot ◽

Trajectory Planning ◽

Kinematic Model ◽

Close Connection ◽

Wheel Slip ◽

Robot Architecture ◽

Experimental Campaign ◽

Base Concept ◽

Kinematic Behaviour

In this paper, the effects of wheel slip compensation in trajectory planning for mobile tractor-trailer robot applications are investigated. Firstly, a kinematic model of the proposed robot architecture is marked out, then an experimental campaign is done to identify if it is possible to kinematically compensate trajectories that otherwise would be subject to large lateral slip. Due to the close connection to the experimental data, the results shown are valid only for Epi.q, the prototype that is the main object of this manuscript. Nonetheless, the base concept can be usefully applied to any mobile robot subject to large lateral slip.

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Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

Journal of Advanced Transportation ◽

10.1155/2020/1456207 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Shan Fang ◽

Lan Yang ◽

Tianqi Wang ◽

Shoucai Jing

Keyword(s):

Fuel Consumption ◽

Trajectory Planning ◽

Signalized Intersections ◽

Planning Method ◽

Signalized Intersection ◽

Driver Model ◽

Connected Vehicles ◽

Connected Vehicle ◽

Traffic Lights ◽

Trigonometric Model

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

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