Smooth and time-optimal S-curve trajectory planning for automated robots and machines

Fang, Yi; Hu, Jie; Liu, Wenhai; Shao, Qi; Qi, Jin; Peng, Yinghong

doi:10.1016/j.mechmachtheory.2019.03.019

Cited by 160 publications

(99 citation statements)

References 43 publications

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“…On the other hand, the maximum velocity reached for the motion profile with a factor γ = 1 5 is ω d2 = 1.736 rad/s. The magnitude of the velocity obtained in [33] is bigger compared with the results obtained with the two factors proposed. The same occurred when the desired position was changed.…”

Section: Simulation and Resultscontrasting

confidence: 65%

“…Unlike the jerk values obtained with the Yi Fang and Wenhai Liu algorithm J max = 30 rad/s 3 , the proposed algorithm performed a jerk value J max = 29.36 rad/s 3 . On the other hand, the acceleration stage in [33] is compounded by seven intervals, while the proposed method in this paper has three phases showing better simulation results. The length of the acceleration phase allows to reduce or increase the magnitude of the jerk, for a factor γ < 2 5 the magnitude increases, while for a factor γ = 2 5 the magnitude decreases, these factors can be chosen by the path planning designer.…”

Section: Simulation and Resultsmentioning

confidence: 87%

“…The algorithm proposed in this paper has been compared with the results presented in [33], where a fourth order polynomial S-curve is developed, the input data has been displayed in Table 2. For simulation, two lengths of acceleration phase were used.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…By one hand, a wide stage of acceleration was chosen with a factor γ = 2 5 ; on the other hand, a short stage of acceleration with a factor γ = 1 5 was used to compare the jerk response of both motion profiles. Besides, two target positions were chosen and taken from simulation section of [33], θ d1 = 2π 3 and θ d2 = π 3 . In Figure 6b, one can see the behavior of the velocity.…”

Section: Simulation and Resultsmentioning

confidence: 99%

See 3 more Smart Citations

A New Seven-Segment Profile Algorithm for an Open Source Architecture in a Hybrid Electronic Platform

2019

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The velocity profiles are used in the design of trajectories in motion control systems. It is necessary to design smoother movements to avoid high stress in the motor. In this paper, the rate of change in acceleration value is used to develop an S-curve velocity profile which presents an acceleration and deceleration stage smoother than the trapezoidal velocity profile reducing the error at the end of the duty-cycle pre-established in one degree of freedom (DoF) application. Furthermore, a new methodology is developed to generate a seven-segment profile that works with negative velocity and displacement constraints applying an open source architecture in a hybrid electronic platform compounded by a system on a chip (SoC) Raspberry Pi 3 and a field programmable gate array (FPGA). The performance of the motion controller is measured through the comparison of the error obtained in real-time application with a trapezoidal velocity profile. As a result, a low-cost platform and an open architecture system are achieved.

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Section: Simulation and Resultscontrasting

confidence: 65%

Section: Simulation and Resultsmentioning

confidence: 87%

Section: Simulation and Resultsmentioning

confidence: 99%

Section: Simulation and Resultsmentioning

confidence: 99%

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A New Seven-Segment Profile Algorithm for an Open Source Architecture in a Hybrid Electronic Platform

2019

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“…The fifth-order B-spline interpolation method is used to interpolate the motion trajectory in the joint space, and then the trajectory is optimized by the elite non-dominated sorting genetic algorithm (NSGA-II) for trajectory planning of the surgical robot [8]. In 2019, Yi Fang et al proposed a smooth and time-optimal S-curve trajectory planning method to meet the requirements of the high-speed and ultra-precise operation of robotic manipulators in modern industrial applications [9]. However, these methods do not consider the influence of the unstructured obstacle environment.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

Zhou

et al. 2020

Applied Sciences

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In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.

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Traditional versus fuzzy optimal coupling in multi‐axis distributed motion control: a pre‐IoT‐ integration procedure

Soliman

Reddy

2020

Adv Control Appl

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For the Internet of Things (IoT)-enabled motion control, the controller's efficient behavior should be justified before the IoT-integration procedure. With the IoT revolution, the remote control of multimotors in the industry can take place. For this aim, a set of four permanent magnetic DC (PMDC) motors is considered in the current study as one of the industrial applications in which the speed synchronization of the motors is required. Optimal individual systems controller tuning is performed using the Particle swarm optimization technique which is also used for the coupling gains and scaling factors tuning of the traditional and fuzzy cross-coupling strategies, respectively, as proposed methods. A comparison between the two proposed coupling strategies and the two existing traditional and fuzzy coupling strategies is carried out as a pre-IoT-integration procedure while considering a load implementation on each motor. The comparison procedure takes place in terms of the individual motors performance indices values and the synchronization error performance indices between every two systems. The obtained results show that the proposed fuzzy cross-coupling strategy gives smaller indices and therefore better synchronization error than the proposed traditional and the other two existing strategies making it more suitable for the IoT-Integration process. A proposed scheme for the IoT-integration is presented.

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Smooth and time-optimal S-curve trajectory planning for automated robots and machines

Cited by 160 publications

References 43 publications

A New Seven-Segment Profile Algorithm for an Open Source Architecture in a Hybrid Electronic Platform

A New Seven-Segment Profile Algorithm for an Open Source Architecture in a Hybrid Electronic Platform

Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

Traditional versus fuzzy optimal coupling in multi‐axis distributed motion control: a pre‐IoT‐ integration procedure

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