Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)
This work addresses path planning for autonomous MAVs in unknown environments, representing an incremental improvement over existing RRT*-based methods.
The researchers tackled path planning for multirotor aerial vehicles by modifying RRT* with adaptive search space and steering functions, then applying kinodynamic smoothing to generate dynamically feasible, obstacle-free trajectories. They tested the approach in simulated environments, though no specific performance metrics were provided.
We explore path planning followed by kinodynamic smoothing while ensuring the vehicle dynamics feasibility for MAVs. We have chosen a geometrically based motion planning technique \textquotedblleft RRT*\textquotedblright\; for this purpose. In the proposed technique, we modified original RRT* introducing an adaptive search space and a steering function which help to increase the consistency of the planner. Moreover, we propose multiple RRT* which generates a set of desired paths, provided that the optimal path is selected among them. Then, apply kinodynamic smoothing, which will result in dynamically feasible as well as obstacle-free path. Thereafter, a b spline-based trajectory is generated to maneuver vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments.