Landing route planning method for micro drones based on hybrid optimization algorithm - Shaoran Liang; Bifeng Song; Dong Xue (Biomimetic Intelligence and Robotics, 2021)
Aiming at the obstacle avoidance trajectory planning problem in the landing process of the micro drone, this paper proposes a swarm optimization algorithm that combines the dragonfly optimization method and the differential evolution method. The orthogonal learning mechanism is adopted to realize the adaptive switch between the two algorithms. In the process of landing route planning, the planning plane is first obtained by making the gliding plane tangent to the obstacle. In the planning plane, the projection of obstacle is transformed into multiple unreachable line segments. By designing an optimization model, the 3D landing route planning problem is transformed into a 2D obstacle avoidance route optimization problem. Taking the shortest route as the optimization objective, the penalty factor is introduced into the cost function to avoid the intersection of the landing route and obstacle. During the optimization process, through the orthogonal learning of the intermediate iterative results, the hybrid algorithm can adaptively select the next iterative algorithm, so it can give full play to the respective advantages of the two algorithms. The optimization results show that, compared with the single optimization algorithm, the hybrid optimization algorithm proposed in this paper can better solve the problem of landing route planning for micro-small UAVs.