Abstract
Rapidly-exploring Random Trees (RRTs) have been widely used for motion planning problems due to their ability to efficiently find solutions. Informed RRT* is an optimized version of RRT, which not only implements the rewiring process to optimize the tree but also limits the search area to a subset of the state space to return near-optimal solutions faster. However, limiting the state space is a function of the obtained shortest path so that before a solution is found, the planner cannot limit the state space to a subset. Moreover, unidirectional RRTs such as Informed RRT* take more time to find initial solutions in comparison to the bidirectional RRTs. In this paper, we propose Hybrid RRT, which divides the planning process into three parts: finding initial solutions by a dual-tree search, combining two trees into one, and optimizing the solution. In order to obtain an initial solution, Hybrid RRT implements a dual-tree search, which helps it find solutions faster than unidirectional searches. Then, it combines the start tree and the goal tree of the dual-tree search into one so as to implement informed sampling for a single tree to optimize the current solution. The simulation carried out in Open Motion Planning Library (OMPL), which shows that Hybrid RRT achieved outstanding improvement over RRT* and Informed RRT*.
Highlights
MOTION planning is involved in various applications such as Unmanned Aerial Vehicles (UAVs), Autonomous Underwater Vehicles (AUVs), driver-less cars, virtual prototyping, biology, and computer graphics [1]–[8]
We introduce a single-query semibidirectional planning method for optimal motion planning problems called Hybrid Rapidly-exploring Random Trees (RRTs), which divides the planning time into three phases
SIMULATION Hybrid RRT was compared to other RRT-based methods on simulated problems in R3 and R6 using Open Motion Planning Library (OMPL) [25]
Summary
MOTION planning is involved in various applications such as Unmanned Aerial Vehicles (UAVs), Autonomous Underwater Vehicles (AUVs), driver-less cars, virtual prototyping, biology, and computer graphics [1]–[8]. R. Mashayekhi et al.: Hybrid RRT: A Semi-Dual-Tree RRT-Based Motion Planner such as Probabilistic Road Map (PRM) [13] can solve several problems with different start locations and goal locations in the state space. Informed RRT* [11], [17] solves this problem of RRT* by limiting the search area to a subset of the state space so as to return near-optimal solutions faster than the standard version of RRT*. In order to achieve fast results out from the optimization process, Hybrid RRT limits the state space into a subset of the state space like Informed RRT* It needs to combine two trees of phase one into one tree to be able to implement Informed sampling on a single tree.
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