New potential functions for mobile robot path planning

By Shuzhi Sam Ge, Youjing Cui. IEEE Trans. Robotics Autom., 2000

732 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/aed61480df3e0a16a95bf764782a3ce4e13a4071

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

1. Khac Duc Do
2. Juntong Qi
3. Jianda Han
4. Jizhong Xiao
5. Aude Gemma Billard
6. Xia Yong
7. Dalei Song
8. Liang Yang

In Computer Vision and Pattern Recognition

1. Khac Duc Do
2. Juntong Qi
3. Jianda Han
4. Jizhong Xiao
5. Aude Gemma Billard
6. Nadia Figueroa
7. Xia Yong
8. Dalei Song

In Control and Systems Engineering

1. Paolo Boscariol
2. Renato Vidoni
3. Albano Lanzutti
4. Khac Duc Do
5. Aude Gemma Billard
6. Nadia Figueroa
7. Takateru Urakubo
8. Seyed Sina Mirrazavi Salehian

Citations in the corpus

• Path Planning and Trajectory Planning Algorithms: A General Overview (2015, methodology): “In [39, 42] alternative applications of the artificial potential method are presented.”
• Modified Newton's method applied to potential field-based navigation for mobile robots (2006, background): “, generalized Gaussian, parabolic model, and generalized sphere world) that we have studied in this paper can be found in [2], [5], and .”
• Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey (2014, unknown): “These can also be used as path planning approaches, essentially by using more information about the environment (see, e.g., [90, 295]).”
• Bounded Controllers for Formation Stabilization of Mobile Agents With Limited Sensing Ranges (2007, background): “The decentralized approach usually involves a combination of agent based local potential fields (e.g., [2], [12], and [ 13 ]).”
• Survey of Robot 3D Path Planning Algorithms (2016, background): “This function generates a unique global minimum value; thus it is able to yield a global feasible path that jumps out of local minima, and [45, 46] put it to a higher stage.”
• A unified framework for coordinated multi-arm motion planning (2018, background): “…et al. 2009; Vahrenkamp et al. 2012; Escande et al. 2007; Vahrenkamp et al. 2010; Escande et al. 2014; Chr ` etien et al. 2016) and (ii) reactive approaches which solve collision-avoidance through the IK problem online (Ge and Cui 2000; Santis et al. 2007; Sugiura et al. 2007; Fang et al. 2015).”
• Formation Tracking Control of Unicycle-Type Mobile Robots With Limited Sensing Ranges (2008, background): “, [2], [7], [11], [13], [14], and [17]) or fully actuated vehicles [6] (which can be converted to double-integrator dynamics via a feedback linearization) were investigated.”
• Feedback stabilization of a nonholonomic system with potential fields: application to a two-wheeled mobile robot among obstacles (2015, methodology): “The potential field method is a powerful method to solve motion planning with obstacle avoidance for mobile robots, where an artificial potential function is constructed according to obstacles [1–4].”