Minimal representation multisensor fusion using differential evolution

Fusion of information from multiple sensors is required for planning and co ntrol of robotic systems in complex environments, The minimal representatio n 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 representa tion multisensor fusion and evaluate a differential evolution approach to t he search for solutions, Experiments in robot manipulation using both tacti le and visual sensing demonstrate that this algorithm is effective in findi ng useful and practical solutions to this problem for real systems. Compari son of this differential evolution algorithm with more traditional genetic algorithms shows distinct advantages in both accuracy and efficiency.