Kinodynamic RRT*: Asymptotically optimal motion planning for robots with linear dynamics

By Dustin J. Webb, Jur van den Berg. IEEE International Conference on Robotics and Automation, 2013

435 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/12118c5bb693ad6e0b15c3e655f4fd3cb367e22e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

RAPTOR: Robust and Perception-Aware Trajectory Replanning for Quadrotor Fast Flight (2020, background): “Searching a kinodynamic path [40, 41] may sufﬁce, but it takes excessive time to obtain a promising path with boundary state constraints at the start and end of the replanned trajectory.”
Sampling-Based Robot Motion Planning: A Review (2014, background): “They are limited to systems with linear dynamics [206], [207], whose cost functions are well known and can be computed between any two states.”
Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation (2021, background): “For such a task, creating a new graph using RRT* [10] that considers the agent’s dynamics is often the preferred approach [15], [19].”
Heuristic approaches in robot path planning: A survey (2016, methodology): “The kinodynamic RRT*, an incremental sampling-based approach for asymptotically optimal motion planning for robots with linear dynamics is introduced in [35].”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “An analytical method for solving a two point boundary value problem subject to kinodynamic constraints was presented in [244], which could be used for finding optimal state-to-state connections and NN searching but was limited to systems with linear dynamics.”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, background): “distance and steering functions for non-holonomic systems by locally linearizing the system dynamics [118] or by deriving a closed-form solution for systems with linear dynamics [119].”
Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions (2016, background): “Adapted RRT* [64], [49] Non-holonomic Offline Car-like and UAV Uniform A* Heuristic”