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Automatic path planning is an essential aspect of unmanned aerial vehicle (UAV) autonomy. This paper presents a three dimensional path planning algorithm based on adaptive sensitivity decision operator combined with particle swarm optimization (PSO) technique. In the proposed method, an adaptive sensitivity decision area is constructed to overcome the defects of local optimal and slow convergence. By using this specified area, the potential particle locations with high probabilities are determined and other candidates are deleted to improve computational capacity. Then the searching space of particles is constrained in a limited boundary to avoid premature state. In addition, the searching accuracy is enhanced by the relative particle directivity from current location. The objective function is redesigned by taking into account the distance to destination and UAV self-constraints. To evaluate the path length, the paired-sample T-Test is performed and the straight line rate ( SLR ) index is introduced. In the two scenarios applied in this paper, our proposed method is 35.4 % , 21.6 % and 49.5 % better compared with other three tested optimization algorithms in the path cost on average. Correspondingly it is 9.6 % , 12.8 % , and 25.3 % better in SLR , which is capable of generating higher quality paths efficiently for UAVs. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are frequently utilized for transferring and delivering. Dual- or multi-quadrotor cooperative transportation schemes are required for heavy loads. In this work, focusing on the path planning for a dual-quadrotor cooperative transport system, a unique artificial potential field-A*(APF-A*) algorithm is studied to fulfill the safe and effective cooperative suspension transport. APF-A* algorithm proposed the strategy of host-wingman cooperative transportation based on the APF theory, and optimizes the heuristic function by introducing the concept of repulsive force to maintain the quadrotor flying at a safe distance from the obstacle. In addition, APF-A* algorithm promises to increase search efficiency while identifying the optimal path, and it can reduce the swing angle motion of loads to enhance the security of transportation. The coordinated flight control and path planning of quadrotor UAVs are simulated in AirSim environment and tested in flight experiments. The results demonstrate that the algorithm is capable to plan safe and reliable paths, meanwhile changing formation and avoid collision when the wingman is close to obstacles. In comparison to the regular A* algorithm, the APF-A* algorithm proposed in the paper performs better in path planning for dual-quadrotor cooperative transport system, reducing the search time and the number of search grids by nearly 60% and 53.75%, respectively. [ABSTRACT FROM AUTHOR]

Unmanned combat aerial vehicle (UCAV) technology has to address many challenges in complex battlefield environments. To produce a safe and low-energy flying path, a UCAV requires many points to build a path that avoids threats, which increases the problem dimension, consumes more computational resources and makes the results unstable. To address this issue, this paper employs a new model known as double-layer coding (DLC) for path planning with unevenly distributed points to decrease the number of superfluous points on the path. Meanwhile, the RPSO algorithm, which introduces a novel strategy of rotating particles in high-dimensional space to search for targets, is proposed as an enhanced particle swarm optimization (PSO) algorithm. The proposed method effectively improves PSO ′ s exploration capacity. Furthermore, RPSO is employed to implement the double-layer coding model for path planning (DLCRPSO). The experimental results show that the proposed DLCRPSO method for path planning always produces feasible flight paths in complex environments. [Display omitted] • A double-layer coding model for path planning is established. • An improved PSO with a novel exploration strategy is proposed. • The superiority of the double-layer coding model used improved PSO is verified. [ABSTRACT FROM AUTHOR]