Kinodynamic Motion Planning: A Novel Type Of Nonlinear, Passive Damping Forces And Advantages
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2016.
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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 1; 2022: 4; 2023: 0; 2024: 0; 2025: 2.
8 papers.
- 2022Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networkscited by 16
- 2021MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraintscited by 7
- 2022Model-free Neural Lyapunov Control for Safe Robot Navigationcited by 5
- 2018Discontinuity-Sensitive Optimal Control Learning by Mixture of Expertscited by 2
- 2022L4KDE: Learning for KinoDynamic Tree Expansioncited by 2
- 2022Lazy Probabilistic Roadmaps Revisitedcited by 0
- 2025Accelerating db-A* for Kinodynamic Motion Planning Using Diffusioncited by 0
- 2025Unified Linear Parametric Map Modeling and Perception-aware Trajectory Planning for Mobile Roboticscited by 0
- 2025Unified Linear Parametric Map Modeling and Perception-aware Trajectory Planning for Mobile Robotics12 Jul 2025
- 2025Accelerating db-A* for Kinodynamic Motion Planning Using Diffusion7 Mar 2025
- 2022Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks17 Oct 2022
- 2022Lazy Probabilistic Roadmaps Revisited28 Sep 2022
- 2022Model-free Neural Lyapunov Control for Safe Robot Navigation2 Mar 2022
- 2022L4KDE: Learning for KinoDynamic Tree Expansion2 Mar 2022
- 2021MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints17 Jan 2021
- 2018Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts7 Mar 2018