A novel algorithm is presented in this paper for motion planning of an unmanned aerial vehicle (UAV) from a start position to a goal position in a two-dimensional environment cluttered with stationary obstacles. The algorithm, termed 'GSE', leverages a generalized shape expansion (GSE)-based sampling strategy, the main contribution of the paper, to explore the workspace efficiently. Once the shortest path is found from start position to goal position, a locally optimal trajectory is obtained within the homotopy class using sequential convex programming. Numerical simulations on the performance of the GSE algorithm and comparison of the same with that of some existing well-established algorithms are performed. The computational efficiency of the GSE algorithm is found to be significantly higher than that of the algorithms in comparison, while the trajectory costs obtained by the GSE algorithm is found to be marginally better in comparison with others. © 2019 American Automatic Control Council.