Bio-inspired 4D Trajectory Generation For Multi-UAV Cooperation

In time-critical cooperative missions such as simultaneous attack and formation flight, the members of an Unmanned Aerila Vehicle (UAV) group need to arrive at the destinations at the expected time. Thus the cooperative four-demisional (4D, three demisional position and time) trajectory generation technique has received more and more interest. The use of 4D trajectory can lower the uncertainty of trajectories, improve the reliability of mission execution, utilize airspace effectively, and achieve better guidance capability. The research about the cooperative 4D trajectory generation has great theoretical significance and vast application prospects.The general tau theory is a bio-inspired 4D motion planning theory, in which the time for closing the gaps between current and goal states is used for 4D motion guidance. The tau guidance strategies based on the general tau theory can synchronously plan the time-variant position and velocity with more succinct expressions. Hence these strategies can fit for the requirements of cooperative 4D trajectory generation.This thesis aims at the research of cooperative 4D trajectory generation method for multiple UAVs. The tau guidance strategies are used into the multi-UAV application scenarios. To fit the 4D trajectory generation of multi-UAVs more properly, two new tau guidance strategies are proposed. Based on the newly presented strategies, the cooperative 4D trajectory generation methods using centralized optimzation, distributed planning and multi-agent Q learning are studied. Furthermore, the tau guidance vector field is proposed utilizing position-time synchronous capability of tau strategies, and a method based on the tau vector field is designed for cooperative standoff tracking of moving targets.The main work and contributions of this thesis are stated as follows:1. The current research status of cooperative multi-UAV 4D trajectory generation is reviewed. The research history and advances of the bio-inspired general tau theory and tau guidance strategies are comprehensively overviewed, and the disadvantages and application scenarios of the tau strategies are summarized.2. To overcome the disadvantages of the existing tau strategies, the better tau guidance strategies are researched. Firstly, combining the bio-inspired general tau theory with the property of harmonic motion, a new strategy named intrinsic tau harmonic (tau-H) guidance strategy is proposed. The 4D trajectory generated by this strategy not only achieves zero initial acceleration, but also enhances shape adjustment capability for de-confliction. Secondly, to meet the requirements of decentralized cooperative missions like flight formation and target tracking, the improved intrinsic tau gravity (tau-G) guidance strategy is proposed. By adding an initial velocity into the intrinsic movement of original tau-G strategy, the improved tau-G strategy can synchronously guide both position and velocity to their expectations exactly at the desired time.3. A centralized collision-free 4D trajectory generation method based on the tau-H strategy is presented for multi-UAVs. This method utilizes the tau-H strategy to generate 4D trajectories, formulates the centralized trajectory generation problem and optimizes it by the modified Particle Swarm Optimization. Furthermore, the conflict detection and resolution approaches are applied to ensure flight safety. Numerous simulation results of the simultaneous arrival missions demonstrate that the proposed method based on tau-H strategy has a better convergence and a stronger conflict resolution capability, and can provide more flyable and safer 4D trajectories.4. Based on the improved tau-G strategy, a decentralized 4D trajectory generation method is designed. Particularly, this method divides the multi-UAV trajectory optimzation problem into several local problems, which are solved by every vehicle according to its own states and the information of neighbors. The decentralized receding horizon optimization is applied to renew trajectory parameters step by step to correct the errors repeatedly. To deal with the environmental uncertainty, the receding optimization is driven by not only time-sampling but also conflict events. The dynamics simulation results of challenging time-constrained tasks show that this decentralized trajectory generation method can efficiently provide safer and lower-cost trajectories for UAVs.5. Combining the improved tau-G strategy with multi-agent Q learning, a 4D trajectory generation method is proposed for multi-UAVs. In this method, the wire fitting neural network Q learning is used to learn the trajectory generation tasks, while the learning of multiple UAVs is orginized by the win or learn fast-policy hill climbing approch. To enhance the task adaptability of the trajectory generation method, the UAVs exchange the outstanding learning experience with their neighbours. This trajectory generation method based on multi-agent Q learning is verified by a simulation with comprehensive flight tasks. The simulation results demonstrate that the proposed method can efficiently provide the flyable and safe 4D trajectories for distributed multi-UAV system.6. To fuse the time dimension into vector field guidance (VFG), the tau vector field guidance (rVFG) method is presented. With the help of the intrinsic tau-G guidance strategy, rVFG acquires the capability of guiding the UAVs to approach the target circle exactly at the desired time. Furthermore, on the basis of τVFG, a comprehensive standoff tracking method for multiple UAVs is proposed. In the simulation of multi-UAV cooperative standoff tracking, the τVFG can perform better in cooperative 4D guidance of multiple UAVs with lower computation load, smaller tracking errors, better flyability, and higher flight safety.