Autonomous Robotic Exploration by Incremental Road Map Construction

In this paper, we propose a novel path planning framework for autonomous exploration in unknown environments using a mobile robot. A graph structure is incrementally constructed along with the exploration process. The structure is the road map that represents the topology of the explored environment. To construct the road map, we design a sampling strategy to get random points in the explored environment uniformly. A global path from the current location of the robot to the target area can be found on this road map efficiently. We utilize a lazy collision checking method that only checks the feasibility of the generated global path to improve the planning efficiency. The feasible global path is further optimized with our proposed trajectory optimization method considering the motion constraints of the robot. This mechanism can facilitate the path cost evaluation for the next best view selection. In order to select the next best target region, we propose a utility function that takes into account both the path cost and the information gain of a candidate target region. Moreover, we present a target reselection mechanism to evaluate the target region and reduce the extra path cost. The efficiency and effectiveness of our approach are demonstrated using a mobile robot in both simulation and real experimental studies. Note to Practitioners This paper is motivated by the efficient exploration problem, which plays a key role in various areas such as the information gathering and environment monitoring. In these applications, the mission length and executing time are often restricted by the battery capacity of the robot. In this paper, an efficient path planning framework is introduced to reduce the path length and exploration time. The robot keeps a road map of the environment to facilitate the path planning. The proposed target selection mechanism helps the robot determine the next best target to explore. Furthermore, the proposed trajectory optimization algorithm helps in reducing the path cost. Overall, this framework enables efficiently and autonomously exploration with a novel path planning framework.