LAUSR

This paper presents a study on short-term cooperative conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs). A two-layered CDR mechanism is proposed, which aims at guaranteeing safe separation, minimizing the overall cost of UAVs, and improving computational efficiency. In the first layer, the information from the environment is processed. In the second layer, conflicts among UAVs are resolved by applying the local centralized optimization method, with consideration given to the dynamic constraints of UAVs. This paper studies the safe separation constraints of pairwise conflicts in virtue of a geometry-based method. A heading change and speed change mixed conflict resolution approach is applied. To meet with the online planning requirements, the vectorized stochastic parallel gradient descent-based method is proposed to find the local optimal heading change solutions. The linear safe separation constraints on speeds are derived. A periodicity feature-based method is used to depart the feasible sub-regions for each pairwise conflict. A mixed integer linear programming model is established to find the optimal speed change solutions. The experiments results show that the proposed heading change algorithm could greatly reduce the summation of additional flight distances of UAVs, and influences on air traffic, compared with other short-term algorithms; the computational efficiency of this algorithm satisfies the online planning requirement. Comparing with the existing algorithm, our speed change algorithm reduces the number of feasible sub-regions to $2^{n_{c} }$ times lower, where $n_{c}$ is the number of pairwise conflict, and therefore, it reduces computation time dramatically.