Novel hybrid optimization algorithm using PSO and MADS for the trajectory estimation of a four track wheel skid-steered mobile robot

Abstract

Several optimization algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization, have previously been applied in order to reliably obtain more accurate trajectory estimation for mobile robot. However, these optimization algorithms can get easily trapped in local optima when solving a complex system, which has many local optima and many input variables. This paper proposes a novel hybrid optimization algorithm-based tuning of the extended Kalman filter, which involves the PSO and mesh adaptive direct search algorithms, prior to operation. As demonstrated by our experimental results, the advantages of the novel hybrid optimization algorithm resolve the limitations of other algorithms in the trajectory estimation of a four track wheel skid-steered mobile robot (4-TW SSMR).