Core Concepts
This article presents Greedy RRT* (G-RRT*), an asymptotically optimal sampling-based planning algorithm that leverages greedy heuristics and bidirectional search to quickly find high-quality solutions in complex high-dimensional state spaces.
Abstract
The article addresses the computational burden associated with the informed hyperellipsoid proposed in Informed RRT* by introducing a new direct informed sampling procedure. The proposed approach, Greedy RRT* (G-RRT*), biases the sampling based on the heuristic information of the states in the current solution path, regardless of the cost, to mitigate the impact of tortuous initial solution paths.
Key highlights:
G-RRT* maintains two rapidly growing trees, one rooted in the start and one in the goal, and uses a greedy connection heuristic to guide the trees towards each other to find initial solutions quickly.
It introduces a greedy informed set, a subset of the informed set, which greedily exploits the information from the current solution path to rapidly reduce the size of the informed exploration hyperellipsoid, enhancing sampling efficiency.
G-RRT* applies the direct informed sampling technique to the greedy informed set to focus the search on the promising regions of the problem domain based on heuristics, accelerating the convergence rate.
The article proves the completeness and asymptotic optimality of G-RRT* and demonstrates its benefits through simulations and experiments on a self-reconfigurable robot, Panthera, showing improved success and convergence rates compared to state-of-the-art algorithms.
Stats
The planning problems are tested with the objective of minimizing path length.