Ship meteorological navigation is based on hydrometeorological data of a certain time scale, considering the ship’s motion characteristics and its own characteristics. First, we provide the best route for the ship and then use real-time local weather information to correct the route during the ship’s navigation. It can also be expressed as follows: it is based on the hydrological and meteorological conditions of the ship during its voyage and the seakeeping characteristics of the ship itself, and the route planning method is used to select the best route for the ship. The best route is a balance between economy and safety, that is, based on ensuring the safety of ship navigation, the route that meets the shortest navigation time, the least fuel consumption, or the least navigation risk is obtained. Weather navigation includes the optimization of the initial route before sailing and the correction of the route after sailing. As there may be errors in hydrometeorological forecasts, especially in the accuracy and real-time performance of medium and long-term forecasts, the optimal initial route may not achieve the best results. Therefore, after the ship sails, it is necessary to adjust and correct the preferred initial route based on the meteorological information detected by the sensors or the continuously updated hydrometeorological forecast data to ensure the best effect of meteorological navigation. This paper proposes a weather route planning method based on the improved A-star algorithm. The convex shape of the concave obstacle and the expansion of the obstacle are carried out; according to the position of the target point relative to the starting point, the search direction of the A-star algorithm at each node is restricted, and an improved A-star algorithm is proposed. The simulation of global weather route planning shows that the improved A-star algorithm can not only find the optimal path but also effectively reduce the number of nodes that the algorithm needs to search during operation. Compared with the classic algorithm, the improved algorithm reduces the number of node searches by 29.25%.

The construction and running of hydropower plants does not merely involve hydropower generation itself but also facilities surveillance, water quality monitoring, harmful creatures tracking, etc., thus requiring mobile and collaborative monitoring capabilities. MANET (Mobile Ad Hoc Networks), with its mobility, flexibility, and robustness in volatile networking environment, is a competitive candidate to fulfill such tasks. Nevertheless its distributed structure prevents the effective collaboration between MANET nodes. SDN (Software-Defined Networking) provides the centralized control over the network underlay. This paper proposes the SDN-controlled IEEE 802.11 MANET swarm for mobile monitoring in hydropower plants. First, the MANET node is implemented by integrating Raspberry Pi with cameras, various sensors, etc., into the low-cost wheeled mobile hardware to enable sensibility and mobility. Then, multiple such MANET nodes are networked through ad hoc protocols, to construct a flexible and distributed MANET underlay; meanwhile, to implement centralized control over the MANET underlay, every MANET node is equipped with OpenFlow switch software (e.g., Open vSwitch) so that OpenFlow directives issued by the SDN controller can be understood and executed, hence the SDN overlay on top of the MANET underlay. Finally, OpenFlow is extended to offer physical actions such as mobility and sensibility manipulation, beyond pure data forwarding in traditional SDN applications, to achieve mobile monitoring in hydropower plants. The SDN-controlled MANET swarm features mobile and sensing capabilities, flexible networking through ad hoc protocols, and efficient and unified control by SDN. Experiment results prove the feasibility of this network architecture.

In the automatic navigation robot field, robotic autonomous positioning is one of the most difficult challenges. Simultaneous localization and mapping (SLAM) technology can incrementally construct a map of the robot’s moving path in an unknown environment while estimating the position of the robot in the map, providing an effective solution for robots to fully navigate autonomously. The camera can obtain corresponding two‐dimensional digital images from the real three‐dimensional world. These images contain very rich colour, texture information, and highly recognizable features, which provide indispensable information for robots to understand and recognize the environment based on the ability to autonomously explore the unknown environment. Therefore, more and more researchers use cameras to solve SLAM problems, also known as visual SLAM. Visual SLAM needs to process a large number of image data collected by the camera, which has high performance requirements for computing hardware, and thus, its application on embedded mobile platforms is greatly limited. This paper presents a parallelization method based on embedded hardware equipped with embedded GPU. Use CUDA, a parallel computing platform, to accelerate the visual front‐end processing of the visual SLAM algorithm. Extensive experiments are done to verify the effectiveness of the method. The results show that the presented method effectively improves the operating efficiency of the visual SLAM algorithm and ensures the original accuracy of the algorithm.