Dynamic Motion Planning for Mobile Robots Using Potential Field Method

By Shuzhi Sam Ge, Youjing Cui. Auton. Robots, 2002

911 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/0b6f49a442c285790b09f3df873f010630ded3d5

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

Citations in the corpus

Incremental Sampling-based Algorithms for Optimal Motion Planning (2010, background): “These planners demonstrated remarkable performance in accomplishing various tasks in complex environments within reasonable time bounds [14].”
A Multiple-Goal Reinforcement Learning Method for Complex Vehicle Overtaking Maneuvers (2011, neutral): “known steering angle [19], [20].”
Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage (2018, methodology): “The problem can be solved using traditional methods, such as, using Voronoi partitions [2], [3], or using potential field methods [4], [5].”
Sampling-based algorithms for optimal motion planning (2011, background): “These planners demonstrated remarkable performance in accomplishing various tasks in complex environments within reasonable time bounds (Ge and Cui 2002).”
COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields (2018, background): “The τ is a small radius of an artificial safety margin of the OS, which guarantees that at the surface of the obstacles, the repulsive potential is sufficiently large but bounded (Ge and Cui, 2002).”
Path Planning for Autonomous Mobile Robots: A Review (2021, background): “This can be seen in the picture displayed in Figure 5a. Ge and Cui [76] presented an application of APF in environments containing dynamic obstacles.”
Heuristic approaches in robot path planning: A survey (2016, background): “The Vectors for defining the repulsive potential [96].”
A Complete and Scalable Strategy for Coordinating Multiple Robots Within Roadmaps (2008, methodology): “Decoupled methods may use a decentralized architecture, allowing independent planning-based methods such as maze searching [17] or potential fields [3], [8], or they may use a centralized architecture planning for all robots with a single processor.”
Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology (2016, methodology): “Potential field method has been widely used for robot motion planning [157], [158].”