Here we apply our medial axis planning framework to Rapidly Exploring Random Trees (RRTs). RRTs search the planning space by biasing exploration toward unexplored regions. We introduce a novel RRT variant, Medial Axis RRT (MARRT), which biases tree exploration to the medial axis of the free space by pushing all configurations from expansion steps towards the medial axis. We prove that this biasing increases the tree's clearance from obstacles. Improving obstacle clearance is useful where path safety is important, e.g., path planning for robots performing tasks in close proximity to the elderly. We also experimentally analyze MARRT, emphasizing its ability to effectively map difficult passages while increasing obstacle clearance, and compare it to contemporary RRT techniques. MARRT differs from RRT in that instead of growing from the tree directly toward a random configuration, it constrains the growth near the medial axis. It does so by taking a small step towards the random configuration and then pushes the resulting intermediate configuration to the medial axis. It repeats this expansion process until it reaches the maximum expansion length or fails to make progress. Below is an example of this process:
Below are examples of different tree growth in the Tunnel environment. MARRT's growth is nicely constrained to the medial axis.
We also see that MARRT increases both roadmap and path clearances.
