Abstract
Automatic generation of feasible trajectory is one of the key technologies for autonomous flying of unmanned aerial vehicles (UAVs). The existing path planning methods, such as swarm intelligence algorithm and graph-based algorithm, cannot incorporate the flying time and UAV dynamic model into evolution. To overcome such disadvantages, a hierarchical trajectory optimization scheme consisted by improved particle swarm optimization (PSO) and Gauss pseudo-spectral method (GPM) is investigated in this paper. Firstly, considering that traditional GPM is sensitive to initial values, we design an improved PSO for path planning in the first layer. By introducing adaptive parameter adjustment strategy and position mutation updating strategy, the rapidity and optimality of the improved PSO is enhanced. Then in the second layer, a fitted curve based on the path waypoints generated by improved PSO is constructed and served as the initial values for GPM. Comparing with random initial values, the designed curve can significant improve GPM efficiency. A multi-segment strategy is also put forward to further improve the efficiency. Finally, with the consideration of dynamic model and state constraints, the time minimum trajectory planning for quadrotor UAVs is solved. Plenty of simulations are carried out and the results illustrate that the proposed scheme guarantees much better efficiency.
Highlights
Unmanned aerial vehicles (UAVs) are aircrafts without human pilots onboard
In the past two years, the machine learning technique was utilized for UAV trajectory planning [11], [12], but the dynamic model of UAV was not considered
A hybrid hierarchical optimization scheme combined by improved particle swarm optimization (PSO) (IPSO) and Gauss pseudo-spectral method (GPM) is proposed for multiUAV trajectory planning in the paper
Summary
Unmanned aerial vehicles (UAVs) are aircrafts without human pilots onboard. With the development of technology, UAVs have been widely applied to both military and civilian tasks, such as intelligence, surveillance, reconnaissance, rescue and commercial performance [1]–[3]. In [27], [28], a comprehensive analysis on the factors that can affect the performance of PSO was VOLUME 9, 2021 carried out and it was pointed out that parameter selection, topology structure and combination with other methods were the main aspects to improve the efficiency of PSO Taking both advantages of swarm intelligence algorithm and numerical methods, it may provide a meaningful and effective solution for UAV trajectory planning by involving pseudo-spectral methods with swarm intelligence algorithm. It is concluded from the analysis that the generation of favorable initial values for numerical optimization algorithm plays a key role in improving the efficiency and optimality of the trajectory planning Motivated by these aspects, a hybrid hierarchical optimization scheme combined by improved PSO (IPSO) and Gauss pseudo-spectral method (GPM) is proposed for multiUAV trajectory planning in the paper.
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