  Offline procedures for estimating parameters of robot dynamics are
practically based on the parameterized inverse dynamic model. In this paper, we
present a novel approach to parameter estimation of robot dynamics which
removes the necessity of parameterization (i.e. finding the minimum number of
parameters from which the dynamics can be calculated through a linear model
with respect to these parameters). This offline approach is based on a simple
and powerful swarm intelligence tool: the particle swarm optimization (PSO). In
this paper, we discuss and validate the method through simulated experiments.
In Part Two we analyze our method in terms of robustness and compare it to
robust analytical methods of estimation.
