Specifically, the aim of the study is to develop an advanced navigation support system that will lead to autonomous collision avoidance, using rule-based artificial intelligence and deep reinforcement learning algorithm.

This will enable systems to estimate several Obstacle Zones by Target (OZT) among different ships and propose a route that minimizes the risk of a collision.

The partners conducted a demonstration testing along with yo University of Marine Science and Technology’s Shioji Maru, in congested sea areas such as Tokyo Bay as a part of continuous efforts to develop computational algorithms using OZT and avoidance route computational algorithms.

The test confirmed the ability to estimate OZT targeting several target ships in actual operation and develop and suggest avoidance routes in real-time onboard and verified the system’s effectiveness in supporting collision avoidance.

Moreover, the test aimed at developing a collision-avoidance system that realizes medium-to-long-term strategies for avoidance navigation well before target ships pose a risk in congested sea lanes and takes into consideration the experience of maritime officers and other personnel in terms of safety and security.

Concluding, the company joined forces with MOL Marine Co., Ltd., National Maritime Research Institute of National Institute of Maritime, Port and Aviation Technology(MPAT), Tokyo University of Marine Science and Technology, MOL Techno-Trade, Ltd., and YDK Technologies Co., Ltd.