A suitable trajectory in a port inspection mission is important for unmanned aerial vehicles (UAVs). Motion planning can help UAVs quickly generate an optimal trajectory that meets the constraints. The motion planning of UAVs is achieved in this paper as follows: firstly, a collision detection (CD) function is applied that evaluates whether the bias_RRT* (rapidly exploring random tree) algorithm needs to be called. Secondly, an isosceles triangle optimization function optimizes the path. Next, a trajectory is generated based on the minimum snap trajectory method. Lastly, the bias_RRT* algorithm and the improved bias_RRT* algorithm are used in the two experimental scenes for path planning comparison, and trajectory planning is carried out. The results show that, in the improved method, the path length and calculation time are shortened, and the trajectory cost and trajectory deviation are also significantly reduced. Overall, it appears that a camera-equipped UAV under the proposed approach can accomplish monitoring tasks more effectively and safety in port environment.

In many situations, the trajectory of an unmanned aerial vehicle (UAV) is very likely to deviate from the initial path generated by a path planning algorithm. This is in fact due to the existence of dynamic constraints of the UAV. In order to reduce the degree of such a deviation, this research introduces a trajectory planning algorithm, the objective of which is to minimize distance while maintaining security. The algorithm first develops preprocess trajectory points by constructing isosceles triangles then, on the basis of a minimum snap trajectory method, it applies a corridor constraint to an optimization objective function, while the deviation evaluation function is established to quantitatively evaluate the deviation distance. A series of experiments were carried out in a simulation environment with a simplified quay crane model. The results show that the proposed method not only optimizes the time and length of the generated trajectory, but also reduces the average deviation distance by 88.7%. Moreover, the generated trajectory can be well tracked by the UAV through qualitative and quantitative analysis. Overall, the experiments show that the proposed method can generate a higher UAV trajectory quality.