SCHOLARS@TAMU

Randomized motion planning algorithms can be applied to any type of robot, from simple rigid bodies to complex articulated linkages. We abstract the particular motion planning problem into configuration space (C-space) where each point in C-space represents a particular configuration/placement of the robot. Invalid configurations (e.g., in-collision, high energy) become C-obstacles in this higher dimensional space. We then use randomized sampling to construct a graph or tree in C-space and use this data structure to extract feasible trajectories. We explore different general purpose techniques to improve planner performance as well as applications to computational biology.