Trajectory Planning and Control of Multiple Quadcopters for Mars Exploration

The trajectory optimization of multiple quadcopters for Mars exploration has been a challenging task due to a difficult nonconvex space formed by multiple quadcopters in the flight, the complex dynamics model, and complicated obstacle environments. We propose a distributed optimization algorithm (DiPenOpt) using direct collocation methods to solve the optimization in the nonconvex space. The DiPenOpt algorithm contains a penalty function method to transfer the nonconvex space into a convex one and an iterative optimization strategy employing initial value selection methods to enhance the algorithm's convergence rate. We design a position-tracking controller to ensure that the quadcopters can effectively follow trajectories generated by the DiPenOpt, regardless of initial position deviations and uncertainties. We compare the results of the DiPenOpt with other algorithms and find that DiPenOpt has a faster solution speed and shows superior robustness for trajectory optimization of multiple quadcopters in large and complex environments. The simulation results show that the position-tracking controller can ensure error convergence and stabilize the flight path when the quadcopter has an initial error. When exploring Mars with multiple quadcopters, ensuring they move efficiently and safely is critical. Think of it like trying to coordinate several quadcopters in a maze-like environment, where every quadcopter needs its own clear path. Our research introduces a new way (DiPenOpt) to help these quadcopters find their best paths, even in complicated surroundings. Our method makes challenging path-finding problems simpler, and we have added tools to make sure quadcopters stick to their paths, even if they start off a little off-course or have uncertain disturbances. Compared to other methods, DiPenOpt is faster and better suited for situations where there are many quadcopters and obstacles. In simple terms, if we were to send a team of quadcopters to explore Mars, our method would make it easier for them to navigate and provide more reliable results, which is crucial for successful space missions.