Autonomous system (mathematics)

About: Autonomous system (mathematics) is a research topic. Over the lifetime, 1648 publications have been published within this topic receiving 38373 citations.

Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision

Abstract: Autonomous driving in off-road environments requires an exceptionally capable sensor system, particularly given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a complex vision system, which is able to provide the two basic sensorial capabilities needed by autonomous vehicle navigation in extreme environments: obstacle detection and path detection. A variable-width-baseline (up to 1.5 m) single-frame stereo system is used for pitch estimation and obstacle detection, whereas a decision-network approach is used to detect the drivable path by a monocular vision system. The system has been field tested on the TerraMax vehicle, which is one of the only five vehicles to complete the 2005 Defense Advanced Research Projects Agency (DARPA) Grand Challenge course.

An on-line adaptation method in a neural network based control system for AUVs

Abstract: A neural network based control system "Self-Organizing Neural-Net-Controller System: SONCS" has been developed as an adaptive control system for Autonomous Underwater Vehicles (AUVs) In this paper, an on-line adaptation method "Imaginary Training" is proposed to improve the time-consuming adaptation process of the original SONCS The Imaginary Training can be realized by a parallel structure which enables the SONCS to adjust the controller network independently of actual operation of the controlled object The SONCS is divided into two separate parts: the Real-World Part where the controlled object is operated according to the objective, and the Imaginary-World Part where the Imaginary Training is carried out In order to adjust the controller network by the Imaginary Training, it is necessary to introduce a forward model network which can generate simulated state variables without involving actual data A neural network "Identification Network" which has a specific structure to simulate the behavior of dynamical systems is proposed as the forward model network The effectiveness of the Imaginary Training is demonstrated by applying to the heading keeping control of an AUV "Twin-Burger" It is shown that the SONCS adjusts the controller network-through on-line processes in parallel with the actual operation >

An autonomous vision-based mobile robot

Abstract: This paper describes the theoretical development and experimental implementation of a complete navigation procedure for use in an autonomous mobile robot for structured environments. Estimates of the vehicle's position and orientation are based on the rapid observation of visual cues located at discrete positions within the environment. The extended Kalman filter is used to combine these visual observations with sensed wheel rotations to produce optimal estimates continuously. The complete estimation procedure, as well as the control algorithm, developed are time independent. A naturally suitable quantity involving wheel rotations is used as the independent variable. One consequence of this choice is that the vehicle speed can be specified independently of the estimation and control algorithms. Reference paths are "taught" by manually leading the vehicle through the desired path. Estimates produced by the extended Kalman filter during this teaching session are then used to represent the geometry of the path. The tracking of taught reference paths is accomplished by controlling the position and orientation of the vehicle relative to the reference path. Time-independence path tracking has necessitated the development of a novel, geometry-based means for advancing along the reference path. >

Method of constructing a backup path in an autonomous system

Abstract: A method of constructing a backup path in an autonomous system (AS) for failure of an inter-AS link is described. The method comprises identifying an alternate inter-AS path and constructing a tunnel to an end point on the alternate path.

Pilot level of a hierarchical controller for an unmanned mobile robot

Abstract: The controller for an intelligent mobile autonomous system (IMAS), equipped with vision and low-level sensors to cope with unknown obstacles, is modeled as a hierarchy of decision-making for planning and control. One of the levels (pilot) deals with a distorted 'windshield' view of the world and provides the actuator controller with real-time decisions. This level of IMAS controller is treated as a linguistic controller with fuzzy variables that assume values from possible intervals. The decision-making process at this level of control are presented as a production system with a fuzzy database. The rules in the production system are derived from an analytical system model for minimum-time control. The choice of optimal motion execution commands is performed using fuzzy set operators. Also included is a temporal decision-making mechanism (reporter), which recognizes the persisting conflicts between successive levels of the hierarchy by observing the motion trajectory. >