Comparison of Optimal Control Techniques for Learning-based RRT

Kinodynamic motion planning for a robot involves generating a trajectory from a given robot state to goal state while satisfying kinematic and dynamic constraints. Rapidly-exploring Random Trees (RRT) is a sampling-based algorithm that has been widely adopted for this. However, RRT is not fast enough to enable its use in industrial applications....

Kinodynamic Steering using Supervised Learning in RRT

With the need for robots to operate autonomously increasing more and more, the research field of motion planning is becoming more active. Usually planning is done in configuration space, which often leads to non feasible solutions for highly dynamical or underactuated systems. With kinodynamic planning motion can also be planned for this...