Path planning is one of the most important process on applications such as navigating
autonomous vehicles, computer graphics, game development, robotics, and protein folding …

We present a novel optimization-based algorithm for motion planning in dynamic
environments. Our approach uses a stochastic trajectory optimization framework to avoid …

We present a novel algorithm to compute collision-free trajectories in dynamic environments.
Our approach is general and does not require a priori knowledge about the obstacles or …

Background Obtaining atomic-scale information about large-amplitude conformational
transitions in proteins is a challenging problem for both experimental and computational …

Parallel search algorithms harness the multithreading capability of modern processors to
achieve faster planning. One such algorithm is PA* SE (Parallel A* for Slow Expansions) …

For robots using motion planning algorithms such as RRT and RRT*, the computational load
can vary by orders of magnitude as the complexity of the local environment changes. To …

Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning
methods, has been very successful in solving motion planning problems. Even so, sampling …

Lazy search algorithms have been developed to efficiently solve planning problems in
domains where the computational effort is dominated by the cost of edge evaluation. The …

The use of autonomous vehicle/robot has been adopted widely to replace human beings in
performing dangerous missions in adverse environments. Keeping this in mind, path …

This paper addresses the problem of parallelizing the Rapidly-exploring Random Tree
(RRT) algorithm on large-scale distributed-memory architectures, using the message …