Neural Informed RRT*: Learning-based Path Planning with Point Cloud State Representations under Admissible Ellipsoidal Constraints
Zhe Huang,Hongyu Chen,John Pohovey,Katherine Driggs-Campbell,Zhe Huang,Hongyu Chen,John Pohovey,Katherine Driggs-Campbell
Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotic optimality but requires growing the tree uniformly over the free space, which leaves room for efficiency improvement. To accelerate convergence, rule-based informed approaches sample states in an admissible ellipsoidal subset of the space determined by...
