Unmanned aerial vehicle flight time optimal real-time trajectory optimization method capable of ensuring convergence

Abstract

The invention discloses an unmanned aerial vehicle flight time optimal real-time trajectory optimization method capable of ensuring convergence, and belongs to the field of trajectory optimization. According to the method, an unmanned aerial vehicle kinematics model is established under the consideration of gravity action, speed and acceleration factors, and a three-dimensional dimensionless motion equation is established. Constraint conditions of speed and control quantity are established according to specific requirements of obstacle avoidance flight of the unmanned aerial vehicle, and the minimum time is selected as an optimization target to establish an optimal control problem P0 of flight path planning of the unmanned aerial vehicle. Nonlinear dynamics in the problem P0 is transformedinto linear dynamics to obtain a fixed initial and end time trajectory optimization problem P1; and the P1 problem is relaxed into an approximate convex optimization problem P2 through convex relaxation, so that the robustness and robustness of real-time solving of the unmanned aerial vehicle are improved. And the problem P2 is discretized to form a second-order cone programming problem P3, and asecond-order cone programming problem P3 is solved iteratively for finite times to obtain an optimal solution, namely the optimal flight time trajectory of the unmanned aerial vehicle. The task response capability of the unmanned aerial vehicle can be further improved.

Description

technical field [0001] The invention belongs to the field of trajectory optimization, and relates to a real-time trajectory planning method with optimal flight time of an unmanned aerial vehicle, in particular to a real-time obstacle avoidance trajectory optimization method with optimal flight time based on an online convex optimization technology and guaranteed convergence. Background technique [0002] In the past few years, as an aerial robot platform, UAV technology has benefited all aspects of human production and life, and trajectory planning has played a key role in the tasks of UAVs such as load delivery, target search, environmental monitoring, and agricultural plant protection. UAVs need to plan flight trajectories in advance to perform large-scale flight missions. In order to maximize the flight performance of UAVs and improve the agility of UAVs to perform tasks, it is important to quickly plan flight trajectories with minimum flight time. technical status. [0...

AI Technical Summary

Problems solved by technology

Blindly using violent heuristic algorithms is difficult to meet the real-time requirements of agile drones for trajectory optimization; general nonlinear programming algorithms are not always feasible to solve this non-convex problem; sequence optimization based on state iteration or sequence convex The optimization method to solve such problems has the potential of real-time application, but it cannot theoretically ensure the large-scale convergence of the optimization iterative process, so the solution efficiency of this type of algorithm is difficult to be guaranteed theoretically, and it is directly based on the non-convex programming algorithm to solve Although the time-optimal trajectory optimization problem is feasible, the solution efficiency is low and it is difficult to meet the real-time requirements; the Dubins path planning method can give the time-optimal flight path under the condition of two-dimensional plane motion and constant speed. It has good real-time application value, but it cannot handle the situation of three-dimensional motion and variable speed. The path planning method based on spline splicing is difficult to generate a trajectory with optimal time and variable speed.

In addition, the current minimum time trajectory planning algorithm designed by machine learning methods is difficult to deal with small sample trajectories or unmodeled sample trajectories. It is often difficult to explain the solution process of the black box model, and cannot fully verify the flight time in online applications. optimality

Images