Underwater gliders (UGs) represent a class of autonomous underwater vehicles renowned for their extended operational endurance, capable of traversing thousands of kilometers. As the primary environmental factor influencing UG navigation, ocean currents profoundly affect path planning strategies, with temporal variability exhibiting significant regional disparities across marine domains. This study introduces a hybrid path-planning methodology for transoceanic UGs, synergizing classical algorithms with reinforcement learning techniques. The framework initiates by constructing a transoceanic current model through integration of multi-temporal hydrodynamic data from the Global Ocean Physics Analysis and Forecast system. Subsequently, the Dijkstra algorithm generates initial trajectory planning within a static current field derived from long-term averaged flow patterns. Leveraging these preliminary waypoints, a Q-learning algorithm performs segmented trajectory optimization using real-time current data. The refined path undergoes final processing through a smoothing algorithm to yield navigable routes compatible with UG operational constraints. Accompanying this methodology, a dedicated software platform, underwater glider path planning platform (UGPPP), facilitates path planning and performance evaluation. Validation through case studies in the South China Sea, Western Pacific, and transoceanic regions demonstrates the proposed method’s superior overall performance compared to benchmark approaches, achieving a 35.946% hydrodynamic energy utilization rate. This systematic approach establishes a foundational framework for optimizing long-range marine vehicle navigation in dynamic oceanic environments.

This article proposes a novel method for generating the regular virtual tube for safe task execution by UAV swarms in fire dynamic propagation scenarios with static obstacles. Cellular automata is utilized to simulate the dynamic propagation characteristics of fire. We employ the empirical Wang Zhengfei model, taking into account environmental vegetation characteristics, initial wind speed, and wind correction coefficient, to define the state transition function for the cellular automaton. The generation framework for regular virtual tube is divided into two parts: front-end search and back-end optimization. In the front-end, a collision-free trajectory is computed using a search-based approach. In the back-end, a reference curve generation method with soft constraints is employed, and it is solved using gradient information. Subsequently, an optimization method for the radius of the regular virtual tube is provided based on the reference curve. Simulation results demonstrate that the real-time generated regular virtual tube can adapt to dynamic fire scenarios and are more robust in complex obstacle scenarios compared to existing state-of-the-art approaches.

Recently on-demand food delivery service has become very popular in China. More than 30 million orders are placed by eaters of Meituan-Dianping everyday. Delicacies are delivered to eaters in 30 minutes on average. To fully leverage the ability of our couriers and restaurants, delivery scope is proposed as an infrastructure product for on-demand food delivery area. A delivery scope based retrieval system is designed and built on our platform. In order to draw suitable delivery scopes for millions of restaurant partners, we propose a pioneering delivery scope generation framework. In our framework, a single delivery scope generation algorithm is proposed by using spatial computational techniques and data mining techniques. Moreover, a scope scoring algorithm and decision algorithm are proposed by utilizing machine learning models and combinatorial optimization techniques. Specifically, we propose a novel delivery scope sample generation method and use the scope related features to estimate order numbers and average delivery time in a period of time for each delivery scope. Then we formalize the candidate scopes selection process as a binary integer programming problem. Both branch&bound algorithm and a heuristic search algorithm are integrated in our system. Results of online experiments show that scopes generated by our new algorithm significantly outperform manual generated ones. Our algorithm brings more orders without hurt of users' experience. After deployed online, our system has saved thousands of hours for operation staff, and it is considered to be one of the most useful operation tools to balance demand of eaters and supply of restaurants and couriers.