Abstract
Effective motion control could achieve the accurate positioning and fast movement of industrial robotics to improve industrial productivity significantly. Time-optimal trajectory optimization (TO) is a great concern in the motion control of robotics, which could improve motion efficiency by providing high-speed and reasonable motion references to motion controllers. In this study, a new general time-optimal TO strategy, the second-order continuous polynomial interpolation function (SCPIF) combined with the particle swarm optimization with cosine-decreasing weight (CDW-PSO) subject to kinematic and dynamic limits, successfully optimizes the movement time of the PUMA 560 serial manipulator. The SCPIF could be used to generate the second-order continuous movement trajectories of six joints in joint space based on the assigned positions and time intervals. The CDW-PSO algorithm could further search for the optimal movement time subject to the limits of the angular displacement, angular velocity, angular acceleration, angular jerk, and joint torque of the manipulator. Two numerical experiments are conducted to illustrate the generalization ability of the CDW-PSO algorithm. The advantage of the CDW would be reflected by comparing with the random weight (RW), the constant weight (CW), and the linearly decreasing weight (LDW), respectively, in each experiment. The experimental results show that the CDW-PSO algorithm would perform better than the RW-PSO, CW-PSO, and LDW-PSO algorithms in terms of the convergence rate and quality of the convergent solution. The proposed time-optimal TO strategy would be applied to all types of manipulators while the optimized trajectories could be incorporated in the motion controllers of the actual manipulators due to considering the kinematic and dynamic limits.
Highlights
The operational stock of industrial robots in factories was in ascending trend over the last few years and achie ved 2.7 million in 2019 around the world, an 12% increase compared to 2018 [1]
An improved particle swarm optimization (PSO) algorithm considering kinematic and dynamic limits (Table 2) is proposed to optimize the movement time of the trajectories generated by the 4-3-4 polynomial interpolating function (PIF)
Four random positions (A, B, C, and D) are assigned in the reachable region (Fig. 2) in the Cartesian system and the corresponding joint angles can be calculated by using the inverse kinematic function of the Robotics Toolbox, as shown in Table 3. t1, t2, and t3 are manually set to 5, which satisfies the formula (3) and will be further optimized by the CDW-PSO algorithm
Summary
The operational stock of industrial robots in factories was in ascending trend over the last few years and achie ved 2.7 million in 2019 around the world, an 12% increase compared to 2018 [1]. Trajectory planning can shorten movement time, improve tracking accuracy, and reduce wear of the manipulators by providing high-speed and smooth motion references to the motion controller. The TO convert the raw path to an optimized trajectory, which can reduce movement time and avoid unnecessary jitter and impact of the manipulators [2]. Time-optimal TO algorithms can be used to find the fastest movement trajectory while respecting kinematic or dynamic limits. An improved PSO algorithm with the limits of the angular displacement, angular velocity, angular acceleration, angular jerk, and joint torque is proposed to optimize the movement time of trajectories. The finding of this study will build a time-optimal TO strategy by which the generated trajectories could be incorporated in the motion controller of the actual manipulator
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