Navigational analysis of a humanoid using genetic algorithm with vision assistance

In this paper, a novel vision assisted genetic algorithm based navigational controller has been designed for smooth and collision-free path generation of a humanoid robot. Here, sensory information regarding the nearest obstacle distance and path left to the destination are considered as the inputs to the genetic algorithm controller, and necessary turning angle is generated as the required output to avoid the obstacles present in the path and advance towards the destination. The vision based technique is integrated along with the sensor based navigational model to assist in deciding a safe direction of turn in case the humanoid encounters a dead end situation while negotiating with complicated obstacle settings. The developed model has been verified by navigational analysis of a NAO humanoid in a V-REP simulation arena. The simulation results are also validated against an experimental set-up prepared under laboratory conditions that resembles the simulation arena. The results obtained from both the platforms are compared in terms of selected navigational parameters, and a close agreement has been found between them with a minimal percentage of errors. Finally, the developed model is also evaluated against other existing navigational schemes, and substantial performance improvements have been observed.