Abstract
Many algorithms in probabilistic sampling-based motion planning have been proposed to create a path for a robot in an environment with obstacles. Due to the randomness of sampling, they can efficiently compute the collision-free paths made of segments lying in the configuration space with probabilistic completeness. However, this property also makes the trajectories have some unnecessary redundant or jerky motions, which need to be optimized. For most robotics applications, the trajectories should be short, smooth and keep away from obstacles. This paper proposes a new trajectory optimization technique which transforms a polygon collision-free path into a smooth path, and can deal with trajectories which contain various task constraints. The technique removes redundant motions by quadratic programming in the parameter space of trajectory, and converts collision avoidance conditions to linear constraints to ensure absolute safety of trajectories. Furthermore, the technique uses a projection operator to realize the optimization of trajectories which are subject to some hard kinematic constraints, like keeping a glass of water upright or coordinating operation with dual robots. The experimental results proved the feasibility and effectiveness of the proposed method, when it is compared with other trajectory optimization methods.
Highlights
Sampling-based motion planners (SBMPs), such as Probabilistic Road Maps (Kavraki et al, 1996) (PRMs) or Rapidly-exploring Random Trees (LaValle and Kuffner, 2001) (RRTs), have become the mainstream methods for solving motion planning problems in high-dimensional space because of their high efficiency and probabilistic completeness
This paper introduces a trajectory post-process method that uses linearly constrained quadratic programming to transform a randomly polygonal collision-free trajectory produced by SBMPs into a smoother one
This method combines the advantages of gradient-based optimization (GBO) and Short-cut
Summary
Sampling-based motion planners (SBMPs), such as Probabilistic Road Maps (Kavraki et al, 1996) (PRMs) or Rapidly-exploring Random Trees (LaValle and Kuffner, 2001) (RRTs), have become the mainstream methods for solving motion planning problems in high-dimensional space because of their high efficiency and probabilistic completeness. Most of the cutting-edge motion planning methods like RRT∗ (Karaman and Frazzoli, 2011), Fast Matching Tree (FMT) (Janson et al, 2013) and Stable Sparse RRT* (SST*) (Bekris et al, 2016) are inspired by SBMPs, which can ensure the final trajectory is collision-free. It is necessary to post-process the trajectory generated by SBMPs. Better Collision-Free Trajectory for Robot. The other category is gradient-based optimization (GBO) methods (Ratliff et al, 2009; Kalakrishnan et al, 2011) They treat a trajectory ξ as a point in a possibly infinite-dimensional space, and use the weighted sum of the smooth cost function fsmooth(ξ ) and the obstacle cost function fobs(ξ ) to evaluate the path quality.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have