The generalized Voronoi graph (GVG) is a topological representation of an environment that can be incrementally constructed with a mobile robot using sensor-based control. However, because of sensor range limitations, the GVG control law will fail when the robot moves into a large open area. This paper discusses an extended GVG approach to topological navigation and mapping: the saturated generalized Voronoi graph (S-GVG), for which the robot employs an additional wall-following behavior to navigate along obstacles at the range limit of the sensor. In this paper, we build upon previous work related to the S-GVG and provide two important contributions: 1) a rigorous discussion of the control laws and algorithm modifications that are necessary for incremental construction of the S-GVG with a mobile robot, and 2) a method for incorporating the S-GVG into a novel multi-hypothesis SLAM algorithm for loop-closing and localization. Experiments with a wheeled mobile robot in an office-like environment validate the effectiveness of the proposed approach.

A cooperation approach with consideration of communication limit is proposed for multi-robot area exploration, in which all the robots select local destinations satisfying the constraints on communication range and reach their destinations at the same time to communicate and fuse their map information. Firstly, the robots compute the frontier between the explored region and the unexplored one. The robots choose the optimal frontier points, which maximize information gain, minimize navigation cost and satisfy communication limit as their local destinations. Then the problem of global exploration in unknown environment is converted into that of multi-stage trajectory planning in local known environment. Collision-free, synchronous and separate trajectories are planned for all the robots to realize the limited communication at their destinations. In such a way, efficient and distributed exploration can be achieved. Simulation results are presented to show the effectiveness of our method.

This paper investigates the on-line autonomous distribution for landmarks and the active exploration in environment without or lack of landmarks/features, such as disaster conditions and polar region. In such situation, the robot enters the environment carrying some landmarks and distributes them according to the rules given in this paper. The utility of the landmark distribution is analyzed. Then, based on the extended Kalman filter (EKF), the active exploration is converted into a problem of multi-objective optimization, in which the objective function includes three aspects, i.e. the accuracy of localization and mapping, the predictive area of the unknown environment that will be explored in next step and the information gain provided by the distributed landmarks respectively. The robot chooses the control input that optimizes the objective function such that accurate localization, high-quality mapping and complete exploration will be realized. And then, the supplementation and the redundancy elimination for landmarks are implemented. At last, a set of simulations is presented to show the effectiveness of our approach.