In this work, we develop a novel algorithm for spacecraft and quadrotor trajectory planning in an environment cluttered with many geometrically fixed
obstacles. The Spherical Expansion and Sequential Convex Programming (SE–SCP) algorithm uses a spherical-expansion-based sampling algorithm to explore the workspace. Once a path is found from the start position to the goal position, the algorithm generates a locally optimal trajectory
within the concerned region constrained by the convex optimization problem using sequential convex programming. If the number of samples
tends to infinity, then the SE–SCP trajectory converges to the globally optimal trajectory in the workspace. The SE–SCP algorithm is computationally
efficient, therefore it can be used for real-time applications on resource-constrained systems. Are also presented results of numerical simulations and comparisons with existing algorithms.