Minimal representation multisensor fusion using differential evolution

TLDR

Fusion of multiple sensors is essential for robotic planning and control, and the minimal representation approach uses an information measure as a universal yardstick to integrate diverse data sources. The paper aims to describe minimal representation multisensor fusion principles and evaluate a differential evolution method for solution search. The method was tested in robot manipulation with tactile and visual sensors, showing it can find practical solutions for real systems. The differential evolution algorithm outperforms traditional genetic algorithms in accuracy and efficiency and successfully finds practical solutions in robot manipulation tasks.

Abstract

Fusion of information from multiple sensors is required for planning and control of robotic systems in complex environments. The minimal representation approach is based on an information measure as a universal yardstick for fusion and provides a framework for integrating information from a variety of sources. In this paper, we describe the principles of minimal representation multisensor fusion and evaluate a differential evolution approach to the search for solutions. Experiments in robot manipulation using both tactile and visual sensing demonstrate that this algorithm is effective in finding useful and practical solutions to this problem for real systems. Comparison of this differential evolution algorithm with more traditional genetic algorithms shows distinct advantages in both accuracy and efficiency.