RRT path planning in TP-Space for hybrid navigation
Post date: May 10, 2014 9:56:51 AM
This work addresses hybrid reactive-planned navigation for autonomous vehicles with non-holonomic constraints. Hybrid methods have the potential to combine the strengths of reactive methods, e.g. fast response to dynamic or poorly mapped environments, while avoiding their main pitfalls: (i) the possibility of getting stuck in a local minimum and (ii) not being aware of global path optimality. In order to achieve this objective, we propose extending Rapidly-exploring Random Tree (RRT) planners to Trajectory Parameter Space (TP-Space), previously proposed as an efficient approach to detect collision-free paths of any-shape, kinematically-constrained vehicles. As a result, our proposal generates a tree whose edges are all kinematically-feasible paths, which can be followed by a reactive navigation engine. Our initial experiments demonstrate the suitability of such a hybrid navigator for real time operation with a simulated Ackerman-steering vehicle. Moreover, it is shown how simultaneously employing several families of trajectories to expand the tree improves the obtained plans.