Multilevel Path Planning For Autonomous Vehicles

Keirsey, D.; Mitchell, Joseph S. B.; Payton, David W.; Preyss, Eric P.

doi:10.1117/12.943177

Cited by 11 publications

(1 citation statement)

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“…A number of other models address map building and navigation problems. For instance, Keirsey, Mitchell, Payton, and Preyss (1984) proposed a system capable of planning at three levels. The highest level sets the goal, the second level plans at the long range level, and the lowest level plans routes at the local level avoiding obstacle collision.…”

Section: Discussionmentioning

confidence: 99%

Exploration, Navigation and Cognitive Mapping

Voicu

Schmajuk

2000

Adaptive Behavior

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We present a modified version of Schmajuk and Thieme's (1992) neural network model of spatial navigation. The new model differs from the original in several ways. First, whereas the early model assumed no a priori knowledge of the space to be explored, the present model assumes a representation of the environment as a set of potentially connected locations. Second, whereas in the original model the decision as to what place to move to next is based on the comparison of the predictions of the goal when each of the alternative places is briefly entered; in the present paper this decision is based on the comparison of the activation of each of the alternative places when the goal is activated. Computer simulations show that the present network offers a novel description of latent learning in terms of the competition between exploration and exploitation.

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Section: Discussionmentioning

confidence: 99%

Exploration, Navigation and Cognitive Mapping

Voicu

Schmajuk

2000

Adaptive Behavior

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Homeostatic control for a mobile robot: Dynamic replanning in hazardous environments

Arkin¹

1992

J. Robotic Syst.

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A longstanding goal of robotics has been to introduce intelligent machines into environments dangerous to humans. These environments also pose hazards to the robots themselves. By embedding sensing devices as a means for monitoring the internal state of the robot, dynamic plan reformulation can occur in situations that threaten the existence of the robot. A method exploiting an analogy to the endocrine control system is forwarded as the preferred method for homeostatic control—the maintenance of a safe internal environment for the machine. Examples are given describing the impact of fuel reserve depletion and global temperature stress. A methodology using signal schemas as a means to supplement the existing motor schema control found in the Autonomous Robot Architecture (AuRA) is presented.

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Military applications of expert systems

Gilmore

1985

Future Generation Computer Systems

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Multilevel Path Planning For Autonomous Vehicles

Cited by 11 publications

References 0 publications

Exploration, Navigation and Cognitive Mapping

Exploration, Navigation and Cognitive Mapping

Homeostatic control for a mobile robot: Dynamic replanning in hazardous environments

Military applications of expert systems

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