Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation

This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles

Heading in the right direction : guiding cellular alignment by substrate anisotropy

Energie en entropie sturen cellen in de zelfde richtin

Optic Flow Drives Human Visuo-Locomotor Adaptation

SummaryTwo strategies can guide walking to a stationary goal: (1) the optic-flow strategy, in which one aligns the direction of locomotion or “heading” specified by optic flow with the visual goal [1, 2]; and (2) the egocentric-direction strategy, in which one aligns the locomotor axis with the perceived egocentric direction of the goal [3, 4] and in which error results in optical target drift [5]. Optic flow appears to dominate steering control in richly structured visual environments [2, 6–8], whereas the egocentric- direction strategy prevails in visually sparse environments [2, 3, 9]. Here we determine whether optic flow also drives visuo-locomotor adaptation in visually structured environments. Participants adapted to walking with the virtual-heading direction displaced 10° to the right of the actual walking direction and were then tested with a normally aligned heading. Two environments, one visually structured and one visually sparse, were crossed in adaptation and test phases. Adaptation of the walking path was more rapid and complete in the structured environment; the negative aftereffect on path deviation was twice that in the sparse environment, indicating that optic flow contributes over and above target drift alone. Optic flow thus plays a central role in both online control of walking and adaptation of the visuo-locomotor mapping

New Hampshire Thinks U.S. is on the Right Track After Obama\u27s First 100 Days 4/27/2009

After his first 100 days in office, New Hampshire adults give President Barack Obama high job approval ratings. And for the first time since 2005, a majority of New Hampshire adults think the United States is heading in the right direction

2018 Tangipahoa Parish Casino Project Survey

A survey of 530 randomly selected Tangipahoa Parish registered voters was conducted Monday February 19, 2018 by the University of New Orleans Survey Research Center. Survey respondents were asked if they approved or disapproved of the casino resort project and if they thought Tangipahoa Parish was heading in the right direction or the wrong direction in an interactive voice response telephone survey (IVR)

The Grizzly, March 23, 1993

Ursinus Mourns The Loss of One of Our Own • Worst Storm of the Century • Danceteller Performs at Ursinus • Ursinus\u27s Own Ticketron • Consider Women\u27s Studies • Wismer Rolls Out the Red Carpet • College Needs Policy on Closing • No Class • The Right to Life • U.C. Baseball Heading in Right Direction • Softball Breaks Even in the Carolinas • Three Outstanding Jens • Gymnasts at Nationalshttps://digitalcommons.ursinus.edu/grizzlynews/1312/thumbnail.jp

Autonomous Trail Following

Trails typically lack standard markers that characterize roadways. Nevertheless, trails are useful for off-road navigation. Here, trail following problem is approached by identifying the deviation of the robot from the heading direction of the trail by fine-tuning a pre-trained Inception-V3 [1] network. Key questions considered in this work include the required number, nature and geometry of the cameras and how trail types -- encoded in pre-existing maps -- can be exploited in addressing this task. Through evaluation of representative image datasets and on-robot testing we found: (i) that although a single camera cannot estimate angular deviation from the heading direction, but it can reliably detect that the robot is, or is not, following the trail; (ii) that two cameras pointing towards the left and the right can be used to estimate heading reliably within a differential framework; (iii) that trail nature is a useful tool for training networks for different trail types