Robot motion planning in a changing, partially predictable environment

Steven M Lavalle, Rajeev Sharma

Research output: Contribution to conferencePaper

Abstract

In this paper we present a framework for analyzing and determining robot motion plans for situations in which the robot is affected by an environment that probabilistically changes over time. In general, motion planning under uncertainty has recently received substantial interest, and in particular a changing-environment has been recognized as an important aspect of motion planing under uncertainty. We model the environment as a finite-state Markov process, and the robot executes a motion strategy that is conditioned on its current position and the state of the environment. Optimality of a robot strategy is evaluated in terms of a performance functional that depends on the environment, robot actions, and a precise encoding of relevant preferences. By using a simple, yet powerful computation technique that is based on dynamic programming, we can numerically compute optimal robot strategies for a wide class of problems, surpassing previous results in this context that were obtained analytically. Several computed motion planning examples are presented.

Original languageEnglish (US)
Pages261-266
Number of pages6
StatePublished - Dec 1 1994
EventProceedings of the 1994 IEEE International Symposium on Intelligent Control - Columbus, OH, USA
Duration: Aug 16 1994Aug 18 1994

Other

OtherProceedings of the 1994 IEEE International Symposium on Intelligent Control
CityColumbus, OH, USA
Period8/16/948/18/94

Fingerprint

Motion Planning
Motion planning
Robot
Robots
Motion
Uncertainty
Dynamic programming
Markov Process
Markov processes
Dynamic Programming
Optimality
Encoding
Strategy

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Lavalle, S. M., & Sharma, R. (1994). Robot motion planning in a changing, partially predictable environment. 261-266. Paper presented at Proceedings of the 1994 IEEE International Symposium on Intelligent Control, Columbus, OH, USA, .

Robot motion planning in a changing, partially predictable environment. / Lavalle, Steven M; Sharma, Rajeev.

1994. 261-266 Paper presented at Proceedings of the 1994 IEEE International Symposium on Intelligent Control, Columbus, OH, USA, .

Research output: Contribution to conferencePaper

Lavalle, SM & Sharma, R 1994, 'Robot motion planning in a changing, partially predictable environment' Paper presented at Proceedings of the 1994 IEEE International Symposium on Intelligent Control, Columbus, OH, USA, 8/16/94 - 8/18/94, pp. 261-266.
Lavalle SM, Sharma R. Robot motion planning in a changing, partially predictable environment. 1994. Paper presented at Proceedings of the 1994 IEEE International Symposium on Intelligent Control, Columbus, OH, USA, .
Lavalle, Steven M ; Sharma, Rajeev. / Robot motion planning in a changing, partially predictable environment. Paper presented at Proceedings of the 1994 IEEE International Symposium on Intelligent Control, Columbus, OH, USA, .6 p.
@conference{b9185af5ab8342c5ab1028adef9c7275,
title = "Robot motion planning in a changing, partially predictable environment",
abstract = "In this paper we present a framework for analyzing and determining robot motion plans for situations in which the robot is affected by an environment that probabilistically changes over time. In general, motion planning under uncertainty has recently received substantial interest, and in particular a changing-environment has been recognized as an important aspect of motion planing under uncertainty. We model the environment as a finite-state Markov process, and the robot executes a motion strategy that is conditioned on its current position and the state of the environment. Optimality of a robot strategy is evaluated in terms of a performance functional that depends on the environment, robot actions, and a precise encoding of relevant preferences. By using a simple, yet powerful computation technique that is based on dynamic programming, we can numerically compute optimal robot strategies for a wide class of problems, surpassing previous results in this context that were obtained analytically. Several computed motion planning examples are presented.",
author = "Lavalle, {Steven M} and Rajeev Sharma",
year = "1994",
month = "12",
day = "1",
language = "English (US)",
pages = "261--266",
note = "Proceedings of the 1994 IEEE International Symposium on Intelligent Control ; Conference date: 16-08-1994 Through 18-08-1994",

}

TY - CONF

T1 - Robot motion planning in a changing, partially predictable environment

AU - Lavalle, Steven M

AU - Sharma, Rajeev

PY - 1994/12/1

Y1 - 1994/12/1

N2 - In this paper we present a framework for analyzing and determining robot motion plans for situations in which the robot is affected by an environment that probabilistically changes over time. In general, motion planning under uncertainty has recently received substantial interest, and in particular a changing-environment has been recognized as an important aspect of motion planing under uncertainty. We model the environment as a finite-state Markov process, and the robot executes a motion strategy that is conditioned on its current position and the state of the environment. Optimality of a robot strategy is evaluated in terms of a performance functional that depends on the environment, robot actions, and a precise encoding of relevant preferences. By using a simple, yet powerful computation technique that is based on dynamic programming, we can numerically compute optimal robot strategies for a wide class of problems, surpassing previous results in this context that were obtained analytically. Several computed motion planning examples are presented.

AB - In this paper we present a framework for analyzing and determining robot motion plans for situations in which the robot is affected by an environment that probabilistically changes over time. In general, motion planning under uncertainty has recently received substantial interest, and in particular a changing-environment has been recognized as an important aspect of motion planing under uncertainty. We model the environment as a finite-state Markov process, and the robot executes a motion strategy that is conditioned on its current position and the state of the environment. Optimality of a robot strategy is evaluated in terms of a performance functional that depends on the environment, robot actions, and a precise encoding of relevant preferences. By using a simple, yet powerful computation technique that is based on dynamic programming, we can numerically compute optimal robot strategies for a wide class of problems, surpassing previous results in this context that were obtained analytically. Several computed motion planning examples are presented.

UR - http://www.scopus.com/inward/record.url?scp=0028727255&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028727255&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0028727255

SP - 261

EP - 266

ER -