Path Optimization in Dynamic Adverse Weathers

Abstract

As we all know, weather condition is constantly changing. Then, when an adverse weather event occurs during travelling period, path planning becomes a dynamic path optimization (DPO) problem. A common practice of DPO is to conduct real-time online path optimization based on the current weather condition. However, the result of online optimization under the current weather condition is rarely optimal for future weather conditions. We are concerned with how to achieve optimal actual travelling trajectory by just a single offline optimization, given the dynamics of weather conditions is pre-known. To this end, the concept of co-evolutionary path optimization (CEPO) is introduced, where the weather condition in a single run of offline optimization is not static, but keeps changing during the single run of offline optimization. Existing DPO methods can hardly address CEPO, because they do not allow the weather condition to change in a single run of online optimization. To address the CEPO in dynamical adverse weathers, this paper proposes a ripple-spreading algorithm (RSA), which can achieve optimal actual travelling trajectory by a single offline calculation. The reported CEPO and RSA are then tested on a typhoon scenario in Hainan Province of China, and the advantages against traditional DPO methods are clearly demonstrated.