An adaptive framework for ‘single shot’ motion planning: A self-tuning system for rigid and articulated robots

Daniel Vallejo; I. Remmler; N. M. Amato

doi:10.1109/ROBOT.2001.932524

An adaptive framework for ‘single shot’ motion planning: A self-tuning system for rigid and articulated robots

Daniel Vallejo, I. Remmler, N. M. Amato

Research output: Contribution to journal › Article › peer-review

Abstract

This paper describes an enhanced version of our previously proposed adaptive framework for single shot motion planning. This framework is versitile, and particularly suitable for crowded environments. Our iterative strategy analyzes the characteristics of the query and adaptively selects planners whose strengths match the current situation. Contributions in this paper include an automatic method for setting and adaptively tuning planner characterizations, and reducing the reliance on programmer expertise present in the original framework. The adaptive refinement enables the system to evolve parameters specifically suited for particular classes of applications. The system now supports articulated robots, which were not supported previously. Our experimental results in complex SD CAD environments show that our strategy solves queries that none of the planners could solve on their own.

Original language	English (US)
Pages (from-to)	21-26
Number of pages	6
Journal	Proceedings - IEEE International Conference on Robotics and Automation
Volume	1
DOIs	https://doi.org/10.1109/ROBOT.2001.932524
State	Published - 2001
Externally published	Yes

ASJC Scopus subject areas

Software
Control and Systems Engineering
Artificial Intelligence
Electrical and Electronic Engineering

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10.1109/ROBOT.2001.932524

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AB - This paper describes an enhanced version of our previously proposed adaptive framework for single shot motion planning. This framework is versitile, and particularly suitable for crowded environments. Our iterative strategy analyzes the characteristics of the query and adaptively selects planners whose strengths match the current situation. Contributions in this paper include an automatic method for setting and adaptively tuning planner characterizations, and reducing the reliance on programmer expertise present in the original framework. The adaptive refinement enables the system to evolve parameters specifically suited for particular classes of applications. The system now supports articulated robots, which were not supported previously. Our experimental results in complex SD CAD environments show that our strategy solves queries that none of the planners could solve on their own.

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An adaptive framework for ‘single shot’ motion planning: A self-tuning system for rigid and articulated robots

Abstract

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