Minimizing Driving Risk of Mobile Robots by Combining a Goal Guidance Vector Algorithm with Reactive Navigation

Abstract

In the navigation of mobile robots, the driving risk can be minimized by increasing the probability of success. The algorithm, which is currently commonly known as the shortest path algorithm, performs efficiently, but does not exhibit a good probability of success for achieving the final goal. In this paper, we develop a new reactive navigation algorithm, known as the goal guidance vector (G2V), which can minimize the driving risk within the sensing range. The G2V is designed to improve the performance of the reactive navigation algorithm using a hazard cost function (HCF) that accounts for the scale and locations of the obstacles within the sensing range. We also adopt real-time fuzzy reactive control to determine the weighting factors of the HCF in an unknown environment to determine the optimal G2V. Simulations are conducted to validate the use of this approach for various environments.