An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm's convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e-17 and 10e-18, and the average solution time is 0.05 s and 0.36 s, respectively.

Automated summary

This paper proposes a hybrid equilibrium optimizer slime mould algorithm (EOSMA) to efficiently solve the inverse kinematics problem of complex manipulators. A multi-objective version of EOSMA (MOEOSMA) is also introduced. Experimental results comparing with other algorithms reveal that this method performs well in terms of accuracy and computation time.