Kinodynamic Motion Planning
By A. Masoud. IEEE Robotics Autom. Mag., 2010
307 citations recorded by the source corpus.
This article extends the capabilities of the harmonic potential field (HPF) approach to planning to cover both the kinematic and dynamic aspects of a robot's 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 (NADF). The HPF (harmonic potential field) approach to planning is emerging as a powerful paradigm for the guidance of autonomous agents.
View Paper: https://www.semanticscholar.org/paper/d2627f7b11deab7ffa1b3c034377efa8cf673c9f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- T. Barfoot
- Kostas E. Bekris
- Siddhartha S. Srinivasa
- Mohamed Elbanhawi
- Julius Ziegler
- Jonathan D. Gammell
- M. Simic
- Jur van den Berg
Citations in the corpus
- Asymptotically optimal sampling-based kinodynamic planning (2014, methodology): “To solve such challenges, the idea of kinodynamic planning has been proposed (Donald et al., 1993), which involves directly searching for a collision-free and feasible trajectory in the underlying system’s state space.”
- Kinodynamic RRT*: Asymptotically optimal motion planning for robots with linear dynamics (2013, background): “The term kinodynamic planning was first introduced in 1993 in [4], which presented a resolution-complete algorithm for optimal planning of robots with discretized double integrator dynamics in low-dimensional workspaces.”
- Sampling-Based Robot Motion Planning: A Review (2014, background): “Deterministic planners were proposed, however, they suffered from high computational costs [175], [176].”
- Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs (2014, background): “Bellman [5] referred to this problem as the curse of dimensionality.”
- Optimal trajectory generation for dynamic street scenarios in a Frenét Frame (2010, methodology): “Several methods for trajectory planning have been proposed [11], [19], [2], [4] that find a global trajectory connecting a start and a - possibly distant - goal state.”