Soft computing-based gait planners for a dynamically balanced biped robot negotiating sloping surfaces

Abstract

Dynamically balanced gait generation problems of a biped robot moving up and down the sloping surface have been solved utilizing soft computing-based approaches. The gait generation problem of a biped robot is difficult to model due to its inherent complexity, imprecision in the collected data of the environment, which are the characteristics that can be the best modeled using soft computing. Two different approaches, namely genetic-neural (GA-NN) and genetic-fuzzy (GA-FLC) systems have been developed to solve the ascending and descending gait generation problems of a two-legged robot negotiating the sloping surface. Two modules of neural network (NN)/fuzzy logic controller (FLC) have been used to model the gait generation problem of a biped robot using the GA-NN/GA-FLC system. The weights of the NNs in the GA-NN and knowledge bases of the FLCs in the GA-FLC systems are optimized offline, utilizing a genetic algorithm (GA). Once the GA-NN/GA-FLC system is optimized, it will be able to generate the dynamically balanced gaits of the two-legged robot in the optimal sense.