Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

Abstract

Redundant manipulators play important roles in many industrial and service applications by assisting people fulfill heavy and repetitive jobs. However, redundant manipulators are coupled highlynonlinear systems which exert difficulty of redundancy resolution computation. Conventional methods such as pseudo-inverse-based approaches obtain the resolved joint angles from joint velocity level, which may bring about more computational cost and may neglect joint velocity limits. In this work, a motion planning method based on beetle antennae search algorithm (BAS) is proposed for motion planning of redundant manipulators with the variable joint velocity limit. Such proposed work does not need to resolve the velocity kinematics equation as the conventional methods do, and the proposed method can directly deal with the forward kinematics equation to resolve the desired joint angles. The simulation and experiment on the five-link planar manipulator and the Kuka industrial manipulator system demonstrate the efficiency of the proposed method for motion planning of redundant manipulator, and reveal the reliable performance of the BAS algorithm as compared with genetic algorithm (GA), particle swarm optimization (PSO), firefly algorithm(FA) and quantum behaved particle swarm algorithm(QPSO) methods.

Trajectory Optimization of 5-Link Biped Robot Using Beetle Antennae Search

“…All the physical parameters of robot are given in TABLE II. The objective function along with the constraints is given in ( 11)- (14).…”

A Model-Free Approach for Online Optimization of Nonlinear Systems

“…Most of the real world systems are nonlinear and require optimization of non-convex multimodal function for their operation [1], [2], [3], [4]. With the rise of Machine Learning (ML), the need to develop a numerically efficient optimization method with fast convergence have gained huge attention from researchers [5], [6].…”

Binary Beetle Antennae Search Algorithm for Tangency Portfolio Diversification

“…Because such NLP problems are widely approached by heuristic, a binary beetle antennae search algorithm (BBAS), which is introduced in [8], is employed to provide a solution to the BTPSCC problem. Generally, the beetle antennae search is a memetic meta-heuristic optimization algorithm and has been employed broadly in various scientific fields in the last few years (see [9][10][11][12][13][14][15][16][17]). For example, inline with beetle antennae search (BAS) and enhanced contract net protocol, an assignment framework for fog computing networks is presented in [10].…”