TY  - EJOU
AU  - Zhang, Jinhe 
AU  - Liu, Jie 

TI  - Efficient	Computational	Inverse Method for Positioning Accuracy Estimation	of	Industrial	Robot Under	Stochastic	Uncertainties
T2  - The International Conference on Computational \& Experimental Engineering and Sciences

PY  - 2023
VL  - 25
IS  - 4
SN  - 1933-2815

AB  - The small uncertainties of	 geometric	 parameters	 of	 industrial	 robot, which are	 caused	 by links	
manufacturing and	service	wear	errors,	can	deteriorate the	positioning	accuracy of	end-effector	through	
multi-level	propagation and	is	difficult	 to	be	measured	and	compensated	by	high-precision	instruments.	
Hence,	 an efficient inverse	 identification method of	 parameter uncertainty based	 on global	 sensitivity	
analysis	 and	 optimal	 measurement	 point	 selection is	 proposed. In order	 to	 ensure	 the	 universality of 
identification	results	in	calibration and control works,	the	standard Denavit-Hartenberg (D-H)	method	is	
employed	to	establish the	kinematic	model	of	series 6	degrees	of	 freedom (DOF) robots. Considering the	
stochastic error	between	nominal	structural	parameters	and	actual	ones,	the	mean	and	variance indexes are
used	to	describe	the	uncertainty	of 24	D-H	parameter errors	and	are introduced	to	the	kinematic	model,	and	
then	the	model	is	linearized	to	obtain	the uncertain indexes identification	coefficient	matrix.	It	is	not	feasible	
to	direct identification	the	uncertainty	of	high	dimensional	parameters	from	arbitrary	position.	To	solve	this	
problem,	 Sobol’-based	 sensitivity	 method is	 developed	 to rank	 the	 contribution	 of	 DH	 parameters	 to	
positioning	 accuracy so	 that	 reduce	 redundant	 parameters.	 Simultaneously, an	 orthogonal	 matching	
tracking	method	is	designed	 to	 select	 the	 optimal	measurement	points	 to	 reduce	 the	ill-condition	 of	 the	
matrix.	Then,	the	updated	identification	equation	is	solved	by	inverting.	Finally,	the cases on 6	DOF robot	
indicate	the	effectiveness	of	the	proposed inverse	identification	method.
KW  - Industrial	robot;	positioning	accuracy;	uncertain	inverse	analysis;	sensitivity	analysis

DO  - 10.32604/icces.2023.09279