Environment exploration and map building of mobile robot in unknown environment

Simultaneous environment exploration and map building of a mobile robot in an unknown environment are studied. Based on the real time data acquired from a laser sensor, a suitable environment exploration strategy with obstacle avoidance ability is proposed. To handle the problems existing in producing and evaluating candidates, feasible approaches are proposed. They can maximise the expected information gain and keep the environment information integrated, and ensure the environment exploration's continuum and complete traversal. The new evaluation method overcomes the drawbacks (attend to one criterion and lose another) of the traditional weighted average method. It can comprehensively evaluate the travelling cost, expected information gain and rotating angle to guarantee the quality of the optimal candidate. Furthermore, a topological map model is proposed which uses the nodes of the growing neural gas network as the topological network nodes. Through the growing characteristic of the GNG network, new topological nodes are added into the network to abstract and express the holistic knowledge of the surrounding environment and construct the environment map. Simulation results of two different indoor environments demonstrate its effectiveness and feasibility.