Decentralized 4D Trajectory Generation for UAVs Based on Improved Intrinsic Tau Guidance Strategy

Zuqiang Yang,Ping Li,Zhou Fang

doi:10.5772/63431

Zuqiang Yang, Ping Li + Show 1 more

Open Access

https://doi.org/10.5772/63431

Copy DOI

Abstract

A decentralized four-dimensional (4D) trajectory generation method for unmanned aerial vehicles (UAVs), which uses the improved intrinsic tau gravity (tau-G) guidance strategy, is presented in this paper. Based on general tau theory, the current tau-G strategy can only generate the 4D trajectory with zero initial and final velocities, which is not appropriate for decentralized applications. By adding an initial velocity to the intrinsic movement of tau-G strategy, the improved tau-G strategy can synchronously guide the position and velocity to the desired values at the arrival time. In our decentralized 4D trajectory generation method, the improved tau-G strategy is used to plan the 4D trajectories for UAVs. To deal with environmental uncertainty and communication limitations, the receding horizon optimization driven by both sampling time and conflict events is utilized to renew trajectory parameters continually. The simulation results of challenging time-constrained tasks demonstrate that the proposed method can efficiently provide safer and lower-cost 4D trajectories.

Highlights

In cooperative missions of unmanned aerial vehicles (UAVs), such as simultaneous attack [1] and formation aggregation [2], the members of a UAV group need to arrive at the prescribed destination simultaneously or sequentially. 4D trajectory generation, which adds the time dimension into the three-dimensional (3D) trajectory and accurately controls the arrival time at each waypoint, can dramatically lower trajectory uncer‐ tainty and reliably accomplish the time-critical tasks.Compared with a great number of works on 4D trajectory generation in civil aviation, limited research for UAVs is available in recent literature
The results show that sequential quadratic programming (SQP) costs less time and involves a smaller optimization number than particle swarm optimization (PSO), while the time cost of SQP can meet the requirement of real practice
As the most dangerous vehicle of the two methods is different (UAV5 of I-tau gravity (tau-G)-Decentralized receding horizon optimization (DRHO) and UAV1 of decentralized model predic‐ tive control (DMPC)), the distance curves of the two subfigures change in a different way

Summary

Introduction

Compared with a great number of works on 4D trajectory generation in civil aviation, limited research for UAVs is available in recent literature. As the trajectories for aero‐ buses are often retrieved from the flight database and planned long in advance, they are inappropriate for UAVs to cope with complex missions and online replanning. To meet the arrival time, many approaches adopt speed assignments after planning flyable 3D trajectories. These assignments will lower the flyability of trajectories. It is difficult to optimize some complicated trajectory descriptions for multiple UAVs. In the end, most of the methods in the literature are centralized ones, which require global knowledge of the multi-UAV system and lots of time for optimization

Methods

Results

Conclusion