Collision-avoidance path planning for multi-ship encounters considering ship manoeuvrability and COLREGs

Ship collision prevention has always been a hot topic of research for navigation safety. Recently, autonomous ships have gained much attention as a means of solving collision problems by machine control with a collision-avoidance algorithm. An important question is how to determine optimal path planning for autonomous ships. This paper proposes a path-planning method of collision avoidance for multi-ship encounters that is easy to realize for autonomous ships. The ship course-control system uses fuzzy adaptive proportion-integral-derivative (PID) control to achieve real-time control of the system. The automatic course-altering process of the ship is predicted by combining the ship-motion model and PID controller. According to the COLREGs, ships should take different actions in different encounter situations. Therefore, a scene-identification model is established to identify these situations. To avoid all the TSs, the applicable course-altering range of the OS is obtained by using the improved velocity obstacle model. The optimal path of collision avoidance can be determined from an applicable course-altering range combined with a scene-identification model. Then, the path planning of collision avoidance is realized in the multi-ship environment, and the simulation results show a good effect. The method conforms to navigation practice and provides an effective method for the study of collision avoidance.

Visual features influence thought content in the absence of overt semantic information

It has recently been shown that the perception of visual features of the environment can influence thought content. Both low-level (e.g., fractalness) and high-level (e.g., presence of water) visual features of the environment can influence thought content, in real-world and experimental settings where these features can make people more reflective and contemplative in their thoughts. It remains to be seen, however, if these visual features retain their influence on thoughts in the absence of overt semantic content, which could indicate a more fundamental mechanism for this effect. In this study, we removed this limitation, by creating scrambled edge versions of images, which maintain edge content from the original images but remove scene identification. Non-straight edge density is one visual feature which has been shown to influence many judgements about objects and landscapes, and has also been associated with thoughts of spirituality. We extend previous findings by showing that non-straight edges retain their influence on the selection of a “Spiritual & Life Journey” topic after scene identification removal. These results strengthen the implication of a causal role for the perception of low-level visual features on the influence of higher-order cognitive function, by demonstrating that in the absence of overt semantic content, low-level features, such as edges, influence cognitive processes.