# Kinodynamic Motion Planning: A Novel Type Of Nonlinear, Passive Damping Forces And Advantages

arXiv: https://arxiv.org/abs/1606.09270 · HTML: https://mlsanity.com/paper/1606.09270/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2016.

## Built on by (most cited first, 8 of 8)

- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints](https://mlsanity.com/paper/2101.06798/) (2021)
- [Model-free Neural Lyapunov Control for Safe Robot Navigation](https://mlsanity.com/paper/2203.01190/) (2022)
- [Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts](https://mlsanity.com/paper/1803.02493/) (2018)
- [L4KDE: Learning for KinoDynamic Tree Expansion](https://mlsanity.com/paper/2203.00975/) (2022)
- [Lazy Probabilistic Roadmaps Revisited](https://mlsanity.com/paper/2209.14471/) (2022)
- [Accelerating db-A* for Kinodynamic Motion Planning Using Diffusion](https://mlsanity.com/paper/2503.05539/) (2025)
- [Unified Linear Parametric Map Modeling and Perception-aware Trajectory Planning for Mobile Robotics](https://mlsanity.com/paper/2507.09340/) (2025)

## Abstract (arXiv)

This article extends the capabilities of the harmonic potential field approach to planning to cover both the kinematic and dynamic aspects of a robot motion. The suggested approach converts the gradient guidance field from a harmonic potential to a control signal by augmenting it with a novel type of damping forces called nonlinear, anisotropic, damping forces. The combination of the two provides a signal that can both guide a robot and effectively manage its dynamics. The kinodynamic planning signal inherits the guidance capabilities of the harmonic gradient field. It can also be easily configured to efficiently suppress the inertia-induced transients in the robot trajectory without compromising the speed of operation. The approach works with dissipative systems as well as systems acted on by external forces without needing the full knowledge of the system dynamics. Theoretical developments and simulation results are provided in this article.