Space situational awareness (SSA) plays an important role in maintaining space advantages. Task planning is one of the key technologies in SSA to allocate multiple tasks to multiple satellites, so that a satellite may be allocated to supervise multiple space objects, and a space object may be supervised by multiple satellites. This paper proposes a hierarchical and distributed task-planning framework for SSA systems with focus on fast and effective task planning customized for SSA. In the framework, a global task-planner layer performs satellite and object clustering, so that satellites are clustered into multiple unique clusters on the basis of their positions, while objects are clustered into multiple possibly intersecting clusters, hence allowing for a single object to be supervised by multiple satellites. In each satellite cluster, a local task planner performs distributed task planning using the contract-net protocol (CNP) on the basis of the position and velocity of satellites and objects. In addition, a customized discrete particle swarm optimization (DPSO) algorithm was developed to search for the optimal task-planning result in the CNP. Simulation results showed that the proposed framework can effectively achieve task planning among multiple satellites and space objects. The efficiency and scalability of the proposed framework are demonstrated through static and dynamic orbital simulations.

No abstract

Air-ground coordination systems are usually composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV). In such a system, UAVs can utilize their much more perceptive information to plan the path for UGVs. However, the correctness and accuracy of the planned route are often not guaranteed, and the communication and computation burdens increase with more sophisticated algorithms. This paper proposes a new type of air-ground coordination framework to enable UAVs intervention into UGVs tasks. An event-triggered mechanism in the null space behavior control (NSBC) framework is proposed to decide if an intervention is necessary and the timing of the intervention. Then, the problem of whether to accept the intervention is formulated as an integer programming problem and is solved using model predictive control (MPC). Simulation results show that the UAV can intervene in UGVs accurately and on time, and the UGVs can effectively decide whether to accept the intervention to get rid of troubles, thereby improving the intelligence of the air-ground coordination system.