A new approach to time-optimal trajectory planning with torque and jerk limits for robot

Ma, Jian-wei; Gao, Song; Yan, H.-C.; Lv, Qiujian; Hu, Guo-qing

doi:10.1016/j.robot.2021.103744

Cited by 45 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fourth, the coefficients in the formula (1) can be calculated according to the formula (2). Γ i k is set to a big large real value if the coefficients do not exist.…”

Section: -3-4 Pif Combined With Improved Pso Used In In Joint Spacementioning

confidence: 99%

“…The PG can generate a raw path through or avoid some assigned position points in operating spaces for specified task requirements. The TO then convert the raw path to an optimized trajectory, which can reduce movement time and avoid unnecessary jitter and impact of the manipulators [2]. The conventional TO algorithms only have been used to generate a smooth and continuous movement trajectory, such as polynomial interpolating function (PIF) [3,4], B-spline curve [5,6], NURBS curve [7,8], Bezier curve [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Time-optimal TO algorithms can be used to find the fastest movement trajectory while respecting kinematic or dynamic limits. The dynamic programming (DP) [11,12], quadratic programming (QP) [13,14], and convex optimization (CO) algorithms [2,15] have been researched to optimize the movement time. However, the DP and QP algorithms might consume great memory storage and long computation time to find the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Time-optimal trajectory optimization of serial robotic manipulator with kinematic and dynamic limits based on improved particle swarm optimization

Yang

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Effective motion control can achieve accurate and fast positioning and movement of industrial robotics and improve industrial productivity significantly. Time-optimal trajectory optimization (TO) is a great concern in the motion control of robotics and can improve motion efficiency by providing high-speed and reasonable motion references to the motion controller. In this study, a new time-optimal TO strategy, polynomial interpolation function (PIF) combined with improved particle swarm optimization (PSO) considering kinematic and dynamic limits, successfully optimizes the movement time of the PUMA 560 serial manipulator along a randomly assigned path. The 4-3-4 PIF is first used to generate the smooth and 3-order continuous movement trajectories of six joints in joint space. The PSO with cosine decreasing weight (CDW-PSO) algorithm further reduces the trajectories movement time considering the limits of the angular displacement, angular velocity, angular acceleration, angular jerk, and joint torque. Experimental results show that the CDW-PSO algorithm achieves a better convergence rate of 23 and a better fitness value of 2.46 compared with the PSO with constant weight and linearly decreasing weight algorithms. The CDW-PSO optimized movement time is reduced by 83.6% compared to the manually setting movement time value of 15. The proposed time-optimal TO strategy can be conducted easily and directly search for global optimal solutions without approximation of the limits. The opti-

show abstract

“…Fourth, the coefficients in the formula (1) can be calculated according to the formula (2). Γ i k is set to a big large real value if the coefficients do not exist.…”

Section: -3-4 Pif Combined With Improved Pso Used In In Joint Spacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time-optimal trajectory optimization of serial robotic manipulator with kinematic and dynamic limits based on improved particle swarm optimization

Yang

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Impact-optimal trajectory planning aims to optimize the acceleration of each joint of the manipulator [ 21 ]. Ma et al [ 22 ] proposed a new convex optimization method, which transforms non-convex jerk into linear acceleration and solves the acceleration limitation problem. Dai et al [ 23 ] used a greedy algorithm to optimize the path of a robot with large jitters during manufacturing tasks, so as to improve its trajectory acceleration performance.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Song

Bai

et al. 2021

Entropy

View full text Add to dashboard Cite

Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

show abstract

“…In literature, minimum time path parametrization approaches have been proposed in [9,[31][32][33][34]. In particular, the method developed in [32] has been implemented in [35] with the constraints of the ISO 15066.…”

Section: Introductionmentioning

confidence: 99%

Online Computation of Time-Optimization-Based, Smooth and Path-Consistent Stop Trajectories for Robots

Rojas

Giusti²,

Vidoni³

2022

Robotics

View full text Add to dashboard Cite

Enforcing the cessation of motion is a common action in robotic systems to avoid the damage that the robot can exert on itself, its environment or, in shared environments, people. This procedure raises two main concerns, which are addressed in this paper. On the one hand, the stopping procedure should respect the collision free path computed by the motion planner. On the other hand, a sudden stop may produce large current peaks and challenge the limits of the motor’s control capabilities, as well as degrading the mechanical performance of the system, i.e., increased wear. To address these concerns, we propose a novel method to enforce a mechanically feasible, smooth and path-consistent stop of the robot based on a time-minimization algorithm. We present a numerical implementation of the method, as well as a numerical study of its complexity and convergence. Finally, an experimental comparison with an off-the-shelf stopping scheme is presented, showing the effectiveness of the proposed method.

show abstract

A new approach to time-optimal trajectory planning with torque and jerk limits for robot

Cited by 45 publications

References 18 publications

Time-optimal trajectory optimization of serial robotic manipulator with kinematic and dynamic limits based on improved particle swarm optimization

Time-optimal trajectory optimization of serial robotic manipulator with kinematic and dynamic limits based on improved particle swarm optimization

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Online Computation of Time-Optimization-Based, Smooth and Path-Consistent Stop Trajectories for Robots

Contact Info

Product

Resources

About