A jerk-limited heuristic feedrate scheduling method based on particle swarm optimization for a 5-DOF hybrid robot. (December 2022)

• Record Type: Journal Article
• Authors: Xiao, Juliang; Liu, Sijiang; Liu, Haitao; Wang, Mingli; Li, Guangxi; Wang, Yunpeng
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 78(2022)
• Publication Date: 2022-12
• Subjects: Feedrate scheduling -- Jerk constraints -- Particle swarm optimization (PSO) -- Five-axis machining -- Hybrid robot; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102396
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: Heuristic feedrate scheduling (HFS) method generates a near time-optimal feedrate with low complexity. The HFS method satisfies the jerk constraints of the toolpath and joint systems, and has the potential to solve higherorder constraints. The proposed moving window planning method based on improved PSO achieves the global and local adjustments of control points. The strategies of non-uniform knot insertions reasonably determine the number of control points and knot distribution. The robot achieves short-time and high-stability machining through the HFS method. Abstract: Compared with machine tools, five degrees-of-freedom (DOFs) hybrid robots have been widely concerned in the manufacturing of large complex surface parts due to their characteristics of high flexibility and large workspace. It is of great significance to schedule the time-optimal feedrate that satisfies the high-order constraints (e.g., jerk or jounce) in the toolpath and joint systems to achieve the high-precision and high-efficiency machining of the robot. To overcome the complexity of five-axis feedrate scheduling and improve the optimality of machining time, this paper proposes a jerk-limited heuristic feedrate scheduling (HFS) method with near-optimal time. Firstly, the analytical equations between all constraints and the parametric feedrate are derived, and the mathematical model for control points optimization of the parametric feedrate profile expressed by a B-spline curve is established. Then, combined with the global search particle swarm optimization (GSPSO) algorithm, a proposed moving window planning method optimizes a small number of control points so that the feedrate curve has enough rising space. Subsequently, to obtain the near time-optimal feedrate, the local search PSO (LSPSO) algorithm within the moving window is developed to locally adjust the non-uniformly inserted control points. Compared with the existing methods, the HFS method is beneficial to further optimize the machining time with a faster computation speed, and ensure the stability of the robot machining. Finally, simulations and experiments on the developed TriMule-800 hybrid robot verify the effectiveness of this method.