Search Results (3)

Discussions of autonomous ships are actively being conducted in the industry and by the International Maritime Organization (IMO). In addition, it is anticipated that a significant number of autonomous ships will be operational at sea soon, as a trial run of autonomous ships is underway. Fully autonomous ships will operate based on pre-programmed algorithms to prevent collisions, eliminating the need for onboard navigators or remote operators onshore. Most collision avoidance algorithms are typically based on an engineering approach that predicts the future movement of an approaching ship by observing its vector. However, it is worth noting that even if fully autonomous ships navigate at sea, the majority of ships encountered are still operated by humans. These ships adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG). Therefore, even fully autonomous ships can effectively and legally avoid approaching ships only when they are steered in compliance with the COLREG. However, it has rarely been addressed which procedures should be followed to determine the legally correct action in various situations where fully autonomous ships encounter traditional manned ships. Therefore, this study is divided into two parts. First, a decision-making tree is presented, as simply as possible, to determine the legally correct collision avoidance action according to the COLREG. Secondly, a quantitative analysis is presented for qualitative expressions such as “narrow channel”, “restricted visibility”, and “best aid to avoid collision”. This review will help fully autonomous ships determine legitimate collision avoidance actions and operate safely in seas where human-operated ships are sailing. However, for autonomous ships, the “Trolley problem” and issues related to decision-making for collision avoidance through communication with other ships are left as future challenges. Full article

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches $69.2\%$ of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate. Full article

We propose an algorithm for map-aided inertial dead reckoning (DR) for indoor navigation. The technique is based on a concept of collision avoidance velocity. New positions estimated by DR are checked for collision detection with walls and if such a situation is detected, the collision avoidance velocity is calculated to avert the obstacles. The differences between an individual’s indicated velocity and collision avoidance velocity are then used to correct errors for further path development. The best performance of the proposed technique is achieved when the individual moves along building obstacles. This type of movement is typical for firefighters, during search and rescue operations in thick smoke. Such conditions provided the environment for testing our algorithm. Full article