UAV motion planning and obstacle avoidance based on adaptive 3D cell decomposition: Continuous space vs discrete space

One important challenge in the current research of UAVs is the obstacle avoidance feature, a highly demanded capability in all kinds of UAV applications. Different algorithms that perform sampling tasks in continuous or discrete spaces are widely used in path planning. The Probabilistic Roadmap (PRM) and Rapidly Exploring Random Tree (RRT) variants have a stochastic essence, which allows them to obtain a response even in wide and complex environments. However, they present disadvantages related to computational cost and time convergence of results. In this paper a survey on sampling-based planners is presented, comparing algorithms with continuous and discrete sampling in 3D environments. On the other hand, two new variants are introduced: the Exact Cell Decomposition on Probabilistic Roadmap (ECD-PRM), as an extension of PRM method, and the Modified Adaptive Cell Decomposition (MACD) performing a reduced space decomposition. The comparison is performed attending to several criteria such as path cost, number of generated nodes and number of control points in the final path, whenever it has been reached.