Abstract
Motion planning is a basic problem in many areas, such as robotics, computer games, and animation. It is a challenge to generate a better solution in less time. Line-of-Sight (LoS) is the straight path between two points, and LoS-Check is often used as a path shortcutting method. Lazy evaluation is a successful strategy in reducing the amount of collision detection, which accelerates the motion planning algorithms, especially in high-dimensional spaces. In this paper, we present Lazy LoS-Check (LaLo-Check), which employs a lazy evaluation strategy with the help of a lower bound technique to delay the LoS-Check until it is necessary. The lower bound technique only accepts the states which could provide a better solution, and it is commonly applied in the sample rejection and candidate path selection. Actually, LaLo-Check can be considered as a general path optimization framework, which could be used in most tree-structure motion planners. We choose three representative sampling-based motion planning algorithms, RRT*, Informed RRT*, and BIT*, to evaluate the performance of LaLo-Check. The experimental results show that the new planners which employ the LaLo-Check could find better solutions than the original algorithms within the equivalent time.
Highlights
Motion planning or path planning plays an important role in many areas such as robotic planning, computer animation and games
We take Rapidly-exploring Random Tree (RRT)*-gp [36] into comparison, which uses the LoS-Check without the lazy evaluation strategy
The specific implementation of these algorithms is introduced in the prior sections. All of these planners are implemented on the basis of the Open Motion Planning Library (OMPL) [40], and all of the experiments are executed on a 3.6 GHz Intel Core i7 processor with 4 GB of RAM
Summary
Motion planning or path planning plays an important role in many areas such as robotic planning, computer animation and games. A state in the configuration space may be called a vertex or a point in this paper. A simple description of the motion planning problem is finding a feasible path between two given states in the configuration space, avoiding the collision with obstacles. We state the problem definition and some notation which may be used in this paper. We roughly review how the A* algorithm works because the similar ideas are used in many popular sampling-based planners. Line-of-Sight Check (LoS-Check) is a path shortcutting method and we will roughly describe how to perform Lazy LoS-Check (LaLo-Check) in tree-structure algorithms. The n-dimensional state space is defined as S ⊆ Rn, and we use Sobs to represent the states in the obstacles. A path is defined as a sequence of states that connect sstart and sgoal
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