Efficient Trajectory Planning for UAVs Using Hierarchical Optimization

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.

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

“…The dynamic model for the flock of UAVs was investigated to obtain the efficient trajectory planning via Improved Particle Swarm Optimization (IPSO) and Gauss Pseudo-spectral Method (GPM) 17 . Also, a hierarchical exploitation of swarm UAVs was studied for a reliable communication with ground base stations (GBSs) and a new protocol was introduced via choosing a swarm head among the UAVs to overcome the interference of occupied GBSs 18 .…”

Stability-constrained mobile manipulation planning on rough terrain

“…Inequality (33) shows that the distance between p static zmp and p dynamic zmp during turning motion is proportionally bounded by ( ψ2 + |u ψ |). Then, it can be inferred that there exists a positive value such that p dynamic zmp −p static zmp 2 ≤ σ for an arbitrarily small σ > 0 during turning motion as long as ( ψ2 + |u ψ |) ≤ .…”

“…In recent years, hierarchical planning has been used with some success in autonomous driving and for UAVs, as demonstrated in refs. [30,31,32,33]. However, the aforementioned hierarchical planning examples only apply to the specific applications they were designed for and cannot accommodate the dynamic stability constraint.…”

Motion Planning of UAV Swarm: Recent Challenges and Approaches

“…The results guaranteed the convergence and provided major improvements in energy efficiency and secrecy rate. Shao et al [77] linked multi-segment strategy with improved particle swarm optimization-Gauss pseudo-spectral method (IPSO-GPM) for UAV swarms. The outcomes evaluated that the applied mechanisms increased obtained solution optimality, generated high-quality trajectories, and took minimum running time.…”