RMPD - A Recursive Mid-Point Displacement Algorithm for Path Planning

TL;DR

This paper introduces mRMPD, a hybrid path planning algorithm combining recursive local planning with multi-tree exploration, significantly reducing planning time in complex environments.

Contribution

The paper presents mRMPD, a novel hybrid path planning algorithm that integrates recursive mid-point displacement with multi-RRT, improving efficiency in obstacle-rich spaces.

Findings

mRMPD reduces planning time by up to 80% compared to basic RRT.

The hybrid approach balances exploration and exploitation effectively.

mRMPD adapts to different planning problem complexities.

Abstract

Motivated by what is required for real-time path planning, the paper starts out by presenting sRMPD, a new recursive "local" planner founded on the key notion that, unless made necessary by an obstacle, there must be no deviation from the shortest path between any two points, which would normally be a straight line path in the configuration space. Subsequently, we increase the power of sRMPD by using it as a "connect" subroutine call in a higher-level sampling-based algorithm mRMPD that is inspired by multi-RRT. As a consequence, mRMPD spawns a larger number of space exploring trees in regions of the configuration space that are characterized by a higher density of obstacles. The overall effect is a hybrid tree growing strategy with a trade-off between random exploration as made possible by multi-RRT based logic and immediate exploitation of opportunities to connect two states as made possible by sRMPD. The mRMPD planner can be biased with regard to this trade-off for solving different kinds of planning problems efficiently. Based on the test cases we have run, our experiments show that mRMPD can reduce planning time by up to 80% compared to basic RRT.