A Reactive Lazy PRM Approach for Nonholonomic Motion Planning

Sánchez, Abraham; Cuautle, Rodrigo; Zapata, René; Osorio, Maria A.

doi:10.1007/11874850_58

Cited by 7 publications

(2 citation statements)

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“…This allows the global property of the sampling-based planner to be maintained, while gaining the rapid reactionary planning of the local planner. Sanchez et al [40] implemented a reactive lazy PRM planner that models a Deformable Virtual Zone (DVZ) around the robot. When dynamic obstacles impeach on the virtual zone, control commands are generated to move away from them.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Recent progress on sampling based dynamic motion planning algorithms

Short

Pan

Larkin

et al. 2016

2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored. Review of Sampling Based Dynamic Motion Planning AlgorithmsAndrew Short, Zengxi Pan, Nathan Larkin and Stephen van DuinAbstract-This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored.

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Section: Hybrid Methodsmentioning

confidence: 99%

Recent progress on sampling based dynamic motion planning algorithms

Short

Pan

Larkin

et al. 2016

2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

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“…This feature differentiates the aforementioned RRT algorithm from other path planning algorithms, like PRM or potential fields. Due to their structure, these algorithms do not seem ideal for kinodynamic planning, though they have been adapted in [9] and [10] under specific assumptions. The RRT algorithm for kinodynamic systems is presented in Fig.…”

Section: Systemmentioning

confidence: 99%

Expert-Guided Kinodynamic RRT Path Planner for Non-Holonomic Robots

Sanz

Hernani

Zaragoza

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, an Expert-Guided Kinodynamic RRT algorithm (EGK-RRT) is presented. It aims to consider how a human pilot would navigate a kinodynamic robot. One of the characteristics of this algorithm is the fact that, unlike the original RRT for kinodynamic systems, it generates deterministic control sequences which can be reproduced as long as the sequence of references (sampled states) are known. Here, the performance of the proposed algorithm is tested against the basic RRT, showing that the EGK-RRT greatly improves in terms of execution speed. In addition to this, the influence of using a visibility check and an inertia estimation in order to select the nearest neighbor is also analyzed, demonstrating that a combination of both factors leads to a better overall performance, both in execution speed and in quality of the generated path.

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