Self-driving vehicles are a maturing technology with the potential to reshape mobility by
enhancing the safety, accessibility, efficiency, and convenience of automotive transportation …

During the last decade, sampling-based path planning algorithms, such as probabilistic
roadmaps (PRM) and rapidly exploring random trees (RRT), have been shown to work well …

The Rapidly-exploring Random Tree (RRT) algorithm, based on incremental sampling,
efficiently computes motion plans. Although the RRT algorithm quickly produces candidate …

During the last decade, incremental sampling-based motion planning algorithms, such as
the Rapidly-exploring Random Trees (RRTs), have been shown to work well in practice and …

Planning algorithms are impacting technical disciplines and industries around the world,
including robotics, computer-aided design, manufacturing, computer graphics, aerospace …

S-TaLiRo is a Matlab (TM) toolbox that searches for trajectories of minimal robustness in
Simulink/Stateflow diagrams. It can analyze arbitrary Simulink models or user defined …

One of the biggest boosts for innovation in engineering has been the ongoing improvement
of digital processor technology. Connections between physical systems and computing …

Requirement for high accuracy and speed of grasping operation for motion planning is very
important. Motion planning algorithms for avoiding obstacles in narrow channels play a vital …

We present an efficient asymptotically-optimal randomized motion planning algorithm
solving single-query path planning problems using a bidirectional search. The algorithm …

We present a Monte-Carlo optimization technique for finding system behaviors that falsify a
metric temporal logic (MTL) property. Our approach performs a random walk over the space …