An improved Q-learning algorithm with a modified action-value function and reward-penalty scheme generates time-optimal robot trajectories that respect velocity-dependent piecewise-linear torque constraints.
Industrial robot for experiment The industrial robot used for experimental ver ification is a 6-DOF GSK-RB03A1 robot of Guangzhou CNC Equipment Co., Ltd, as shown in Figure 6
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Reinforcement Learning for Robotic Time-optimal Path Tracking Using Prior Knowledge
An improved Q-learning algorithm with a modified action-value function and reward-penalty scheme generates time-optimal robot trajectories that respect velocity-dependent piecewise-linear torque constraints.