Designing two-level rescue depot location and dynamic rescue policies for unmanned vehicles

• Published in: Reliability engineering & system safety 2023-05, Vol.233, p.109119, Article 109119
• Main Authors: Zhao, Xian, Lv, Zuheng, Qiu, Qingan, Wu, Yaguang
• Format: Article
• Language: English
• Subjects: Mission abort; Mission success probability; Rescue; Rescue depots location; System survivability
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2023.109119

•Two-level rescue depot location policy is developed for unmanned vehicle system.•Dynamic rescue policies (mission abort and maintenance policies) are developed.•Mission success probability and system survivability are derived.•A joint optimization model is constructed to determine the optimal policies. Unmanned vehicles are often required to execute critical missions in harsh environment, causing system failure which may occur during the mission execution or rescue procedure. Existing research has focused primarily on policies during mission execution to reduce failure risk. The paper investigates risk evaluation and control policies both in the mission execution and rescue phases, and proposes two-level rescue depot location policies and dynamic rescue policies including mission abort and maintenance policies for unmanned vehicle systems. Specifically, considering multi-state characteristics of unmanned vehicle systems, two-level rescue depots are introduced to satisfy maintenance demands by providing different types of maintenance. To improve survivability in the mission execution, dynamic mission abort policies are executed when the number of failed components reaches predetermined thresholds, and then a rescue procedure is initiated. During the rescue, elaborate designed rescue depots location and dynamic maintenance policies are developed to reduce the risk of failure in the rescue. After a successful rescue, unmanned vehicles can re-attempt missions. The recursive algorithm and discretization algorithm are used to derive and evaluate mission success probability, system survivability, and expected cost of losses, and optimization models based on three indicators are formulated. The superiority of the proposed policies is demonstrated by a case study of a UAV system.