Mobile Robot Autonomous Map Base Building And Environment Cognition

The research of mobile robot involves a lot of knowledge such as intelligent control, computer science, pattern recognition and artificial intelligence, and the technology in this field has become the focus in the field of robotics and automation. Map building is one of the essential tasks for an autonomous mobile robot's navigation and self-location. Environment cognition is a new research focus in the field of robotics as well as an important research project in cognitive science. This paper researches on composite map base building and environment cognition based on laser range finder.The paper introduces the model of SmartROB-2 mobile robot's odometer and laser range finder firstly and use Split-Merge algorithm as fitting straight line method. Experiment results in complex indoor environments show the method's effectiveness, practicability and suitable environment.Composite map base is proposed to describe environment, which includes metric map, topological map and non-regular obstruction area. This paper realizes original laser data preprocessing of non-regular obstruction area using grid-based statistical method and get rid of dynamic obstacles. It also uses the grid-based nearest neighbor clustering algorithm (GNN) to cluster data points and gets the environment description by mathematical analysis on point sets. Composite map base solves that the simple map can not meet the need of robot autonomous complex tasks completing and can adapts the complexity and non-regularity of geometric structure and obstacles in the indoor environment. Simulations and experiment results implemented in complex indoor environments verify the conclusion.This paper extracts several environment fingerprints composed of feature points by using cognitive science and fingerprint identification for reference. It also proposes the simultaneously path planning and environment cognition algorithm (SPAC) to realize the environment fingerprint matching. ICP algorithm is used to adjust the environment fingerprint to the reference template before each matching. The results of simulation implemented and further data analysis demonstrate the proposed method's validity and practicability.