[Paper Review] Use of PSO in Parameter Estimation of Robot Dynamics; Part One: No Need for Parameterization

Hossein Jahandideh, Mehrzad Namvar|arXiv (Cornell University)|Nov 6, 2012

Robotic Mechanisms and Dynamics9 references6 citations

TL;DR

This paper proposes a novel particle swarm optimization (PSO)-based method for estimating robot dynamics parameters without requiring parameterization—eliminating the need to derive a minimal linear parameterization of the dynamics. The approach directly optimizes the full nonlinear dynamic model using PSO, demonstrating accurate parameter estimation in simulated experiments with improved robustness compared to traditional linear methods.

ABSTRACT

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.

Motivation & Objective

• To eliminate the need for parameterization in offline robot dynamics parameter estimation.
• To develop a direct, nonlinear optimization approach using swarm intelligence for parameter estimation.
• To validate the proposed PSO-based method through simulated experiments.
• To lay the foundation for robust, parameterization-free estimation in robot dynamics.

Proposed method

• The method employs particle swarm optimization (PSO) to directly minimize the error between measured and predicted robot dynamics without requiring a linear parameterization.
• PSO treats the full set of dynamic parameters as decision variables in a nonlinear optimization space.
• The objective function is defined as the sum of squared errors between actual and model-predicted joint torques over a set of motion trajectories.
• The approach avoids the need to derive a minimal set of parameters by working directly with the nonlinear inverse dynamics model.
• The algorithm iteratively updates particle positions and velocities based on personal and global best solutions to converge toward optimal parameter values.
• Simulations are conducted on a robot manipulator to evaluate performance under various noise and trajectory conditions.

Experimental results

Research questions

• RQ1 Can PSO be effectively used to estimate robot dynamics parameters without prior parameterization?
• RQ2 How does the performance of the PSO-based method compare to traditional linear parameter estimation techniques?
• RQ3 What is the accuracy and robustness of the proposed method in the presence of measurement noise and nonlinearities?
• RQ4 Does the method maintain stability and convergence across diverse motion trajectories?

Key findings

• The PSO-based method successfully estimates robot dynamics parameters without requiring any parameterization, simplifying the estimation pipeline.
• The approach achieves high estimation accuracy in simulated experiments, with parameter errors consistently below 5% under ideal conditions.
• The method demonstrates robustness to noise and nonlinear effects, outperforming traditional linear methods in noisy environments.
• The absence of a linear parameterization step reduces computational overhead and increases flexibility in model formulation.
• The results confirm that PSO can effectively optimize complex, nonlinear dynamic models directly in parameter space.
• The method is scalable and adaptable to different robot configurations without re-derivation of the dynamic model structure.