Unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been widely used in delivery. In the context of the COVID-2019, in order to control the development of the epidemic, many places have adopted measures to isolate and close the area once a confirmed case is found. While reducing the contact between people, it also blocks the normal driving of vehicles. Only by changing the traditional logistics and distribution methods can customers who have been in a closed and isolated area for a long time be served. Therefore, we use the Cooperative UGV-UAV to achieve it. In this paper, when commanding cooperative UGV and UAV for emergency resource delivery, we mainly focus on two questions: how to accept the operation order (OPORD) from the commander, how to generate a nested vehicle routing planning. We first employ one intelligent task understanding module to drive the intelligent unmanned vehicles to accept and process the C-BML (Coalition Battle Management Language) formatted OPORD with 5W (what, who, where, why, when) elements. Then, we slove the nested vehicle routing planning problem as a mixed integer linear program (MILP) with the outputs of what is the UGV route, what is the UGV sortie, and how to control the customers’ distribution between the UGV and the UAV. Experimental results of random instances and case study show that using the iterative improvement algorithm increase the speed rate of solving more than 10%.

Unmanned ground vehicles (UGVs) have been widely used in security patrol. The existence of two potential opponents, the malicious teammate (cooperative) and the hostile observer (adversarial), highlights the importance of privacy-preserving planning under contested environments. In a cooperative setting, the disclosure of private information can be restricted to the malicious teammates. In adversarial setting, obfuscation can be added to control the observability of the adversarial observer. In this paper, we attempt to generate opponent-aware privacy-preserving plans, mainly focusing on two questions: what is opponent-aware privacy-preserving planning, and, how can we generate opponent-aware privacy-preserving plans? We first define the opponent-aware privacy-preserving planning problem, where the generated plans preserve admissible privacy. Then, we demonstrate how to generate opponent-aware privacy-preserving plans. The search-based planning algorithms were restricted to public information shared among the cooperators. The observation of the adversarial observer could be purposefully controlled by exploiting decoy goals and diverse paths. Finally, we model the security patrol problem, where the UGV restricts information sharing and attempts to obfuscate the goal. The simulation experiments with privacy leakage analysis and an indoor robot demonstration show the applicability of our proposed approaches.