Time-optimal trajectory planning of serial manipulator based on adaptive cuckoo search algorithm

Feeding and unloading operation for part manufacturing are widely applied by industrial robots. In this paper, a set of algorithms has been used to reach higher efficiency and automation of trajectory planning for a 6-DOF integrated serial kinematic manipulator. Depending on the key nodes of the joint angles calculated by a hybrid inverse kinematics algorithm, the continuous quintic B-spline curve algorithm was utilized for planning smooth trajectories of the feeding motion from peer to peer. An adaptive cuckoo search (ACS) algorithm with high efficiency and excellent stability was proposed to minimize the total motion time under strict dynamic constraints. Comparing with 5 commonly used heuristic methods, the ACS algorithm has faster convergence speed and higher accuracy based on the same fitness function. To verify the implementation effect of the strategy, a 1:5 scale experimental platform was designed and built to implement the time-optimal trajectories. The simulations and experiments indicate that these algorithms lead to efficient planning of time-optimal and smooth trajectory in joint space.