Integrated Optimization of Path Planning and Control for UAVs with Obstacle Avoidance

In this paper, we present an integrated optimization method of path planning and control for UAVs, which can guarantee UAV to reach the target point and avoid obstacles with the shortest motion path and the minimum control input. The novelty is that it can solve the path planning problem and optimal control problem simultaneously by designing the control input directly. Inspired by the artificial potential field method and model predictive control, an integrated optimization model is constructed, which takes into account not only the current state but also the motion state in the future prediction step. Due to the complexity of the optimization model and the nonlinearity of the UAV motion prediction model, the traditional quadratic programming method is not available, so the particle swarm algorithm is introduced to solve the optimization model and obtain the control input. The control and planning strategy proposed in this paper is tested in the presence of obstacles. The simulation results prove the feasibility and performance of planning and controlling. At the same time, the comparison simulation with the traditional APF and the improved APF method verifies the advantages of the integrated optimization method.