A global optimal trajectory optimization method with multiple NFZ constraints

The trajectory optimization challenge, especially with multiple no-fly zones (NFZs), often leads to many local optima. Using mixed-integer programming (MIP) improves chances for finding global optima but at the cost of increased computation due to many binary variables. To speed up solving, this study uses variable substitution, which changes NFZ constraints into simpler upper and lower bounds and uses binary variables for path decision, matching the number of NFZs. Inspired by sequential convex programming (SCP), the issue with a mix of strategies that make the problem easier to solve is tackled, leading to the creation of the hybrid iterative convex programming (HICP) algorithm. Testing in different situations verify the effectiveness of the HICP algorithm. The HICP algorithm demonstrates robust adaptability and significant computational efficiency, capable of producing trajectories that navigate around the general NFZ in under 5 seconds, underscoring its substantial practical value.