Dynamical Trajectory Planning for Unmanned Aerial Vehicle in Hollowed Obstacles

The Unmanned Aerial Vehicle (UAV) has tremen-dous obstacle avoidance and crossing in a confined and complex environment. However, in the traditional path planning algorithm, the UAV can easily fall into the local extremum problem and lack planning ability. Therefore, we propose an RRT-BBO algorithm, which combines the Rapidly-exploring Random Tree (RRT) and Biogeography-Based Optimization (BBO). First, an initial point and the end point are set, and its objective to implement the RRT algorithm between these two points to find a feasible path. Then, the BBO algorithm is applied to optimize the obtained trajectory, and finally, the UAV can quickly cross the hollowed obstacles. Computer simulations are conducted to demonstrate the performance of the proposed method.