Perform Navigation Tasks Research Articles

A Case for Changing the National Airspace System from a Magnetic North to a Geographic North Based Model

The Global Positioning System (GPS) is currently playing a major role in aviation navigation and is proposed by the Federal Aviation Administration (FAA) to be the foundation of the future National Airspace System (NAS). The Position, Velocity and Time (PVT) information provided by GPS for navigation purposes is directly based on Geographic North parameters rather than on Magnetic North, which is currently the foundation of the NAS. This paper uniquely addresses the exploration of this relationship between the GPS and Geographic North by applying an experimental research design to analyze the potential benefit of basing the NAS on Geographic North. This study proposes to quantify the benefits of a Geographic North Model by comparing the performance of navigation tasks by university flight students using a Geographic North model versus those using a Magnetic North model. Similar treatments, consisting of navigational training relating to Geographic North for the experimental group and Magnetic North for the control group, were administered to both groups. Identical navigational tasks were then presented to both groups to perform, using their respective models, and data was collected for the dependant variables of accuracy and time of task performance. The statistical tools of Chi-square and two-tail t-tests with alpha of .05 were applied to the data to evaluate the hypothesis that accuracy and time would both improve with the Geographic North model. The Geographic North group did outperform the Magnetic North group for each dependant variable, but the results were found to be statistically significant for only the time-of-task variable.

Spatial performance of unilateral vestibular defective patients in nonvisual versus visual navigation

The purpose of this study was to investigate the effects of unilateral vestibular neurotomy on humans ability to perform navigation tasks. These tasks provided self-motion feedback by way of either locomotor activity only (nonvisual navigation or "locomotor task") or visual motion cues only (visually simulated navigation or "visual task"). After exploration of an environment in which 4 locations were marked by different objects, subjects attempted to navigate to those locations either by reproducing the same paths as those followed during exploration, by reversing routes, or by making spatial inferences (shortcuts). Vestibular defective patients were tested one day before surgical treatment and during the recovery time course following unilateral vestibular nerve lesion (1 week, 1 month, and 3 month later). Their performance was assessed by measuring turn error and distance error in both navigation tasks and was compared to that of control subjects tested 4 times at similar time intervals. Turn error in the reproduction of previously explored routes in the locomotor task was lower in patients before surgery than in controls, suggesting the existence of compensatory processes. In the acute stage (1 week) after unilateral vestibular lesion, turn error was greater in patients than in controls for the highest level of mental representation (spatial inferences or reversing routes); impairment at making accurate rotations had disappeared by 1 month after vestibular lesion in both navigation tasks. These results point to the role of vestibular cues, in interaction with other sensory modalities, in the elaboration of an accurate internal representation of the environment. In addition, they suggest that unilateral suppression of vestibular information would induce transitory spatial memory disorganization at a high level of information processing.

Multi-Objective Path Planning for Autonomous Sensor-Based Navigation

The paper approaches path planning for autonomous mobile robots based on sensor-based navigation behaviors. The aim is to obtain a plan as a sequence of behaviors that does not only consider minimizing the path's length but also adds robustness by dealing with practical limitations introduced by the sensor and robot systems during task execution. The graph-search algorithm PRIMO-A* has been developed to consider multiple objectives ordered by priority. Moreover, a method is introduced to generate the graph that models the set of possible robot behaviors in the working environment. These methods have been applied to a real robot performing navigation tasks in indoor environments

On the role of stored internal state in the control of autonomous mobile robots

This article informally examines the role of stored internal state (that is, memory) in the control of autonomous mobile robots. The difficulties associated with using stored internal state are reviewed. It is argued that the underlying cause of these problems is the implicit predictions contained within the state, and, therefore, many of the problems can be solved by taking care that the internal state contains information only about predictable aspects of the environment. One way of accomplishing this is to maintain internal state only at a high level of abstraction. The resulting information can be used to guide the actions of a robot but should not be used to control these actions directly; local sensor information is still necessary for immediate control. A mechanism to detect and recover from failures is also required. A control architecture embodying these design principles is briefly described. This architecture was successfully used to control real-world and simulated real-world autonomous mobile robots performing complex navigation tasks. The architecture is able to incorporate standard AI planning and world-modeling algorithms into a real-time situated framework.