Joint Trajectory Planning of Transfer Robots Combining Deep Reinforcement Learning and Deformed Quintic Polynomials
Aiming at motion trajectories and time allocation of all joints of transfer robots under the condition of fixed production cycle time,a joint trajectory time allocation model of transfer robots based on deep reinforcement learning combining with deformed quintic polynomials was constructed.A reward function with the target of cycle time require-ments,speed constraints,and acceleration constraints was designed,a neural network was built,and MATLAB/Simulink software was used to obtain time series meeting with the production cycle time and kinematic constraints.A single-arm four-degree-of-freedom transfer robot simulation experiment was used to verify feasibility and effectiveness of the constructed model.The results show that the running time of each joint of the single-arm four-degree-of-freedom transfer robot is 5.89 s,in which the maximum velocity of translation joint 1 and translation joint 3 are respectively 2597.84 mm/s and 1 697.97 mm/s,and the maximum acceleration are respectively 19 532.11 mm/s2 and 31 302.61 mm/s2 respectively.The velocities of rotary joint 2 and rotary joint 4 are equal in magnitude and opposite in direction.Both of the maximum angular velocities are 137.53(°)/s,and both of the maximum angular accelerations are 1 180.51(°)/s2.All of the above do not exceed kinematic constraints.The constructed model can solve the time allocation problem of transfer robot joint trajectory under specified production cycle time,achieve motion balance of all joints during transfer process of transfer robots,and improve stability and effectiveness of transfer robot operation.
robot technologyjoint track planningdeep reinforcement learningtransfer robotproduction cycle time
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