Robot Path Planning in Narrow Environments Using Improved Roadmap Sampling

Abstract

Path planning is a fundamental problem in mobile robots that optimize the path to determine how the robot reaches its goal. In particular sampling-based methods are popular in robotics. However, the sampling-based method is unsuitable for dynamic and narrow environments, unlike the adaptive method due to its prior planning before moving. Moreover, applying the sampling-based method in real-time and dynamic environments becomes more difficult due to its high computational cost. In this paper, we propose an efficient sampling method for roadmap planners to decrease its computational cost and increase the success rate of path planning. The proposed method applies the artificial potential method and map-decomposition method to roadmap planners such as PRM to achieve the goal.