Complex Navigation Tasks Research Articles

Navigating blind people with walking impairments using a smart walker

Navigation in complex and unknown environments is a major challenge for elderly blind people. Unfortunately, conventional navigation aids such as white canes and guide dogs provide only limited assistance to blind people with walking impairments as they can hardly be combined with a walker, required for walking assistance. Additionally, such navigation aids are constrained to the local vicinity only. We believe that technologies developed in the field of robotics have the potential to assist blind people with walking disabilities in complex navigation tasks as they can provide information about obstacles and reason on both global and local aspects of the environment. The contribution of this article is a smart walker that navigates blind users safely by leveraging recent developments in robotics. Our walker can support the user in two ways, namely by providing information about the vicinity to avoid obstacles and by guiding the user to reach the designated target location. It includes vibro-tactile user interfaces and a controller that takes into account human motion behavior obtained from a user study. In extensive qualitative and quantitative experiments that also involved blind and age-matched participants we demonstrate that our smart walker safely navigates users with limited vision.

Domain-Specific Interference Tests on Navigational Working Memory in Military Pilots.

Human navigation is a very complex ability that encompasses all four stages of human information processing (sensory input, perception/cognition, selection, and execution of an action), involving both cognitive and physical requirements. During flight, the pilot uses all of these stages and one of the most critical aspect is interference. In fact, spatial tasks competing for the same cognitive resource cause greater distraction from a concurrent task than another task that uses different resource modalities. Here we compared and contrasted the performance of pilots and nonpilots of both genders performing increasingly complex navigational memory tasks while exposed to various forms of interference. We investigated the effects of four different sources of interference: motor, spatial motor, verbal, and spatial environment, focusing on gender differences. We found that flight experts perform better than controls (Pilots: 6.50 ± 1.29; Nonpilots: 5.45 ± 1.41). Furthermore, in the general population, navigational working memory is compromised only by spatial environmental interference (Nonpilots: 4.52 ± 1.50); female nonpilots were less able than male nonpilots. Also, the flight expert group showed the same interference, even if reduced (Pilots: 5.24 ± 0.92); moreover, we highlighted a complete absence of gender-related effects. Spatial environmental interference is the only interference producing a decrease in performance. Nevertheless, pilots are less affected than the general population. This is probably a consequence of the need to commit substantial cognitive resources to process spatial information during flight.

Goal-directed affordance prediction at the subtask level

Purpose – This paper aims to present a hybrid control approach that combines learning-based reactive control and affordance-based deliberate control for autonomous mobile robot navigation. Unlike many current navigation approaches which only use learning-based paradigms, the authors focus on how to utilize the machine learning methods for reactive control together with the affordance knowledge that is simultaneously inherent in natural environments to gain advantages from both local and global optimization. Design/methodology/approach – The idea is to decompose the complex and large-scale robot navigation task into multiple sub-tasks and use the hierarchical reinforcement learning (HRL) algorithm, which is well-studied in the learning and control algorithm domains, to decompose the overall task into sub-tasks and learn a grid-topological map of the environment. An affordance-based deliberate controller is used to inspect the affordance knowledge of the obstacles in the environment. The hybrid control architecture is then designed to integrate the learning-based reactive control and affordance-based deliberate control based on the grid-topological and affordance knowledge. Findings – Experiments with computer simulation and an actual humanoid NAO robot have demonstrated the effectiveness of the proposed hybrid approach for mobile robot navigation. Originality/value – The main contributions of this paper are a new robot navigation framework that decomposes a complex navigation task into multiple sub-tasks using the HRL approach, and hybrid control architecture development that integrates learning-based and affordance-based paradigms for autonomous mobile robot navigation.

Direct current brain stimulation enhances navigation efficiency in individuals with low spatial sense of direction.

The aim of this study was to evaluate the influence of right versus left temporal transcranial direct current stimulation (tDCS) on navigation efficiency and spatial memory in individuals with low versus high spatial skills. A mixed design administered low (0.5 mA) versus high (2.0 mA) anodal tDCS (within-participants) over the right or the left temporal lobe (between-participants), centered at electrode site T8 (right) or T7 (left). During stimulation, participants navigated virtual environments in search of specified landmarks, and data were logged in terms of current position and heading over time. Following stimulation, participants completed pointing and map-drawing spatial memory tests. Individual differences in sense of direction reliably and inversely predicted navigation advantages in the 2.0 versus 0.5 mA right hemisphere stimulation condition (R=0.45, P<0.01); in other words, individuals with lower sense of direction showed increased navigation efficiency in the 2.0 versus 0.5 mA condition. Spatial memory tests also showed the development of relatively comprehensive spatial memories: bidimensional regression indicated lower distortion in sketch maps drawn following 2.0 versus 0.5 mA right temporal lobe stimulation (F=8.7, P<0.05). Data provide the first demonstration that right temporal anodal tDCS may hold potential for enhancing navigation efficiency in otherwise poor navigators. Data support neuroimaging studies showing the engagement of right temporal brain regions in developing and applying spatial memories during complex navigation tasks, and uniquely suggest that continuing research may find value in optimizing stimulation parameters (intensity, focality) as a function of individual differences.

Cognitive Impairment Assessment through Visuospatial Memory Can Be Performed with a Modified Walking Corsi Test Using the ‘Magic Carpet'

Background: Subjects with mild cognitive impairment (MCI) have disturbances in their spatial navigation abilities and exhibit early deficits in visuospatial short-term memory. The purpose of the present study was to determine whether a quantitative (span score) and qualitative (evaluating navigation strategies used) analysis of the Corsi test (usual condition and complex navigation task) would be useful to reveal cognitive decline. Methods: We evaluated the performance of 15 young adults, 21 healthy elderly subjects and 15 subjects with MCI using the electronic version of the Corsi test (the Modified Corsi Block-Tapping Test, MCBT) and the complex navigation task (the Modified Walking Corsi Test, MWCT). The MWCT, which is an adaptation of the Corsi test, assesses spatial memory when the subject walks in a complex environment. We used Richard et al.'s model [Cogn Sci 1993;17:497-529] to investigate problem-solving strategies during the Corsi tests. Results: The span scores obtained on the MCBT and the MWCT were significantly lower in the healthy elderly subjects (MCBT = 5.0 ± 0.7; MWCT = 4.0 ± 0.7) and the subjects with MCI (MCBT = 4.7 ± 0.8; MWCT = 4.1 ± 0.9) than in the younger adults (MCBT = 6.2 ± 0.6; MWCT = 5.3 ± 1.0). The visuospatial working memory was more impaired in the complex navigation task (MWCT = 4.3 ± 0.9) than in the modified Corsi test (MCBT = 5.3 ± 0.8). Finally, the subjects with greater cognitive impairment were more likely to have inadequate or absence of problem-solving strategies. Conclusions: Investigating the problem-solving strategies used during the MWCT appears to be a promising way to differentiate between the subjects with MCI and the healthy elderly subjects.

Eye Movements in Gaze Interaction

Gaze as a sole input modality must support complex navigation and selection tasks. Gaze interaction combines specific eye movements and graphic display objects (GDOs). This paper suggests a unifying taxonomy of gaze interaction principles. The taxonomy deals with three types of eye movements: fixations, saccades and smooth pursuits and three types of GDOs: static, dynamic, or absent. This taxonomy is qualified through related research and is the first main contribution of this paper. The second part of the paper offers an experimental exploration of single stroke gaze gestures (SSGG). The main findings suggest (1) that different lengths of SSGG can be used for interaction, (2) that GDOs are not necessary for successful completion, and (3) that SSGG are comparable to dwell time selection.

Learning indoor robot navigation using visual and sensorimotor map information

As a fundamental research topic, autonomous indoor robot navigation continues to be a challenge in unconstrained real-world indoor environments. Although many models for map-building and planning exist, it is difficult to integrate them due to the high amount of noise, dynamics, and complexity. Addressing this challenge, this paper describes a neural model for environment mapping and robot navigation based on learning spatial knowledge. Considering that a person typically moves within a room without colliding with objects, this model learns the spatial knowledge by observing the person's movement using a ceiling-mounted camera. A robot can plan and navigate to any given position in the room based on the acquired map, and adapt it based on having identified possible obstacles. In addition, salient visual features are learned and stored in the map during navigation. This anchoring of visual features in the map enables the robot to find and navigate to a target object by showing an image of it. We implement this model on a humanoid robot and tests are conducted in a home-like environment. Results of our experiments show that the learned sensorimotor map masters complex navigation tasks.

From self-assessment to frustration, a small step toward autonomy in robotic navigation.

Autonomy and self-improvement capabilities are still challenging in the fields of robotics and machine learning. Allowing a robot to autonomously navigate in wide and unknown environments not only requires a repertoire of robust strategies to cope with miscellaneous situations, but also needs mechanisms of self-assessment for guiding learning and for monitoring strategies. Monitoring strategies requires feedbacks on the behavior's quality, from a given fitness system in order to take correct decisions. In this work, we focus on how a second-order controller can be used to (1) manage behaviors according to the situation and (2) seek for human interactions to improve skills. Following an incremental and constructivist approach, we present a generic neural architecture, based on an on-line novelty detection algorithm that may be able to self-evaluate any sensory-motor strategies. This architecture learns contingencies between sensations and actions, giving the expected sensation from the previous perception. Prediction error, coming from surprising events, provides a measure of the quality of the underlying sensory-motor contingencies. We show how a simple second-order controller (emotional system) based on the prediction progress allows the system to regulate its behavior to solve complex navigation tasks and also succeeds in asking for help if it detects dead-lock situations. We propose that this model could be a key structure toward self-assessment and autonomy. We made several experiments that can account for such properties for two different strategies (road following and place cells based navigation) in different situations.

Women Who Know Their Place

Differences between men and women in the performance of tests designed to measure spatial abilities are explained by evolutionary psychologists in terms of adaptive design. The Hunter-Gatherer Theory of Spatial Ability suggests that the adoption of a hunter-gatherer lifestyle (assuming a sexual division of labor) created differential selective pressure on the development of spatial skills in men and women and, therefore, cognitive differences between the sexes. Here, we examine a basic spatial skill-wayfinding (the ability to plan routes and navigate a landscape)-in men and women in a natural, real-world setting as a means of testing the proposition that sex-based differences in spatial ability exist outside of the laboratory. Our results indicate that when physical differences are accounted for, men and women with equivalent experience perform equally well at complex navigation tasks in a real-world setting. We conclude that experience, gendered patterns of activity, and self-assessment are contributing factors in producing previously reported differences in spatial ability.

Vertical versus dynamic menus on the world wide web: Eye tracking study measuring the influence of menu design and task complexity on user performance and subjective preference

Web sites need fast and effective navigation systems. An eye tracking laboratory study with n = 120 participants was conducted to compare the influence of different navigation designs (vertical versus dynamic menus) and task complexity (simple versus complex navigation tasks) on user performance, navigation strategy, and subjective preference. With vertical menus, users needed less eye fixations, were faster and more successful. We conclude that, firstly, vertical menus fit better to perception and cognition than dynamic menus, where the navigation items are hidden and must be accessed by an additional mouse click. Secondly, navigation systems should be extended with different kinds of navigation items adapted to the complexity of the users’ navigation tasks, because users tend to switch their navigation strategy when confronted with complex tasks.

Towards affective camera control in games

Information about interactive virtual environments, such as games, is perceived by users through a virtual camera. While most interactive applications let users control the camera, in complex navigation tasks within 3D environments users often get frustrated with the interaction. In this paper, we propose inclusion of camera control as a vital component of affective adaptive interaction in games. We investigate the impact of camera viewpoints on psychophysiology of players through preference surveys collected from a test game. Data is collected from players of a 3D prey/predator game in which player experience is directly linked to camera settings. Computational models of discrete affective states of fun, challenge, boredom, frustration, excitement, anxiety and relaxation are built on biosignal (heart rate, blood volume pulse and skin conductance) features to predict the pairwise self-reported emotional preferences of the players. For this purpose, automatic feature selection and neuro-evolutionary preference learning are combined providing highly accurate affective models. The performance of the artificial neural network models on unseen data reveals accuracies of above 80% for the majority of discrete affective states examined. The generality of the obtained models is tested in different test-bed game environments and the use of the generated models for creating adaptive affect-driven camera control in games is discussed.

Terrain-adaptive bipedal locomotion control

We describe a framework for the automatic synthesis of biped locomotion controllers that adapt to uneven terrain at run-time. The framework consists of two components: a per-footstep end-effector path planner and a per-timestep generalized-force solver. At the start of each footstep, the planner performs short-term planning in the space of end-effector trajectories. These trajectories adapt to the interactive task goals and the features of the surrounding uneven terrain at run-time. We solve for the parameters of the planner for different tasks in offline optimizations. Using the per-footstep plan, the generalized-force solver takes ground contacts into consideration and solves a quadratic program at each simulation timestep to obtain joint torques that drive the biped. We demonstrate the capabilities of the controllers in complex navigation tasks where they perform gradual or sharp turns and transition between moving forwards, backwards, and sideways on uneven terrain (including hurdles and stairs) according to the interactive task goals. We also show that the resulting controllers are capable of handling morphology changes to the character.

Terrain-adaptive bipedal locomotion control

We describe a framework for the automatic synthesis of biped locomotion controllers that adapt to uneven terrain at run-time. The framework consists of two components: a per-footstep end-effector path planner and a per-timestep generalized-force solver. At the start of each footstep, the planner performs short-term planning in the space of end-effector trajectories. These trajectories adapt to the interactive task goals and the features of the surrounding uneven terrain at run-time. We solve for the parameters of the planner for different tasks in offline optimizations. Using the per-footstep plan, the generalized-force solver takes ground contacts into consideration and solves a quadratic program at each simulation timestep to obtain joint torques that drive the biped. We demonstrate the capabilities of the controllers in complex navigation tasks where they perform gradual or sharp turns and transition between moving forwards, backwards, and sideways on uneven terrain (including hurdles and stairs) according to the interactive task goals. We also show that the resulting controllers are capable of handling morphology changes to the character.

Space and time-related firing in a model of hippocampo-cortical interactions

The present work is part of a project aiming at modeling the hippocampo-cortical interactions in order to solve complex navigation tasks. It is the result of a long partnership between neurobiologists and computer scientists. A close feedback loop between in vivo experiments and computer models leads us to produce biologically accurate neural networks used in robotic navigation. Place cells are pyramidal neurons of the hippocampus (Hs) exhibiting high rates of firing at a particular location in the environment, corresponding to the cell’s “place field”. In a task where rats have to navigate to a goal location and wait for a reward, we recently observed the timing prediction capabilities of CA place cells [1]. This “out-of-field” firing was recorded at the goal location, far from the original place fields of the cells. These patterns of activity are suggested to spread from the Hs to the medial prefrontal cortex (mPFC) where similar activities were observed [2]. Methods In a previous model [3], a spectral timing neural network [4] was used to account for the role of the Hs in the acquisition of classical conditioning. The ability to estimate the timing between separate events was then used to learn and predict transitions between places in the environment. We propose a neural architecture based on this work and explaining the out-of-field activities in the Hs along with their temporal prediction capabilities. The model uses the hippocampo-cortical pathway as a means to spread reward signals to entorhinal neurons. Secondary predictions of the reward signal are then learned, based on transition learning, by pyramidal neurons of the CA region. Results The architecture was implemented in a simulated environment. An agent learned to navigate to a goal location and wait for a reward with the supervision of the experimenter. The results match neurophysiological observations in several ways: 1) Recorded neural activities in hippocampal place cells display out-of-field firing, predictive of the reward at the goal location, in addition to the usual firing on the primary place field. 2) Timing-related firing in the Hs is propagated to the mPFC. This is coherent with observations of the abolition of such activity in the mPFC following hippocampal lesions in rats. 3) The mPFC only plays ar ole during the learning phase of the secondary associations. This is consistent with neurophysiological data on the lack of effect of mPFC inactivation on out-of-field firing in the Hs after learning. Our model is realistic and makes testable hypotheses as to the neural pathways involved. In vivo experiments will be conducted to confirm or infirm those hypotheses. Future work involves testing the system on a real robot in large scale environments.

Sequential egocentric strategy is acquired as early as allocentric strategy: Parallel acquisition of these two navigation strategies

At least two main cognitive strategies can be used to solve a complex navigation task: the allocentric or map-based strategy and the sequential egocentric or route-based strategy. The sequential egocentric strategy differs from a succession of independent simple egocentric responses as it requires a sequential ordering of events, possibly sharing functional similarity with episodic memory in this regard. To question the possible simultaneous encoding of sequential egocentric and allocentric strategies, we developed a paradigm in which these two strategies are spontaneously used or imposed. Our results evidenced that sequential egocentric strategy can be spontaneously acquired at the onset of the training as well as allocentric strategy. Allocentric and sequential egocentric strategies could be used together within a trial, and bidirectional shifts (between trials) were spontaneously performed during the training period by 30% of the participants. Regardless of the strategy used spontaneously during the training, all participants could execute immediate shifts to the opposite non previously used strategy when this strategy was imposed. Altogether, our findings suggest that subjects acquire different types of spatial knowledge in parallel, namely knowledge permitting allocentric navigation as well as knowledge permitting sequential egocentric navigation.

Use of optical motion tracking in application development fog surgical planning and navigation

This paper makes an overview of the application development in the field of optical motion tracking accomplished during the recent years in Hungary. The elements of the integration of known optical camera (passive Polaris, Northern Digital Inc.) into navigation studies are surveyed according to standpoint of software engineer: (A) creating a proper software architecture; (B) selection of calibration procedures for navigated tools and anatomical objects; (C) practical solutions for registering the surgical space to diagnostic models; (D) visualization of registered movements of objects in diagnostic volume. The presented procedures are based on two applications: (A) prototyping program to simulate complex navigational and measurement tasks occurring in clinical field (VocPC); (B) commercial application suitable to control navigated drilling in dental implantology (Dental Planner). DOI: 10.17489/biohun/2008/1/21

Functional brain imaging of a complex navigation task following one night of total sleep deprivation: a preliminary study

Several neuroimaging studies have demonstrated compensatory cerebral responses consequent to sleep deprivation (SD), but all have focused on simple tasks with limited behavioral response options. We assessed the cerebral effects associated with SD during the performance of a complex, open-ended, dual-joystick, 3D navigation task (simulated orbital docking) in a cross-over protocol, with counterbalanced orders of normal sleep (NS) and a single night of total SD (approximately 27 h). Behavioral performance on multiple measures was comparable in the two sleep conditions. Functional magnetic resonance imaging revealed multiple compensatory SD > NS cerebral responses, including the posterior superior temporal sulcus [Brodmann area (BA) 39/22/37], prefrontal cortex (BA 9), lateral temporal cortex (BA 22/21), and right substantia nigra. Right posterior cingulate cortex (BA 31) exhibited NS > SD activity. Our findings extend the compensatory cerebral response hypothesis to complex, open-ended tasks.

Motor primitive and sequence self-organization in a hierarchical recurrent neural network

This study describes how complex goal-directed behavior can be obtained through adaptation processes in a hierarchically organized recurrent neural network using a genetic algorithm (GA). Our experiments, using a simulated Khepera robot, showed that different types of dynamic structures self-organize in the lower and higher levels of the network for the purpose of achieving complex navigation tasks. The parametric bifurcation structures that appear in the lower level explain the mechanism of how behavior primitives are switched in a top-down way. In the higher level, a topologically ordered mapping of initial cell activation states to motor primitive sequences self-organizes by utilizing the initial sensitivity characteristics of non-linear dynamical systems. The biological plausibility of the model's essential principles is discussed.

The micronavigation algorithm: A minimalist approach to navigation

Psychologists' debate about the role of knowledge to control actions in living beings have strongly influenced the research in the field of artificial intelligence and, consequently, of robotics. In both fields a focus of the debate has been the relevance (or even the presence) of a mental/internal representation in driving the course of actions of biological/artificial beings. In this paper we show that even very complex navigation tasks within maze-like environments can be carried out by an agent which needs to store just ‘a handful of bytes’ of internal representation about the world in which it is moving. The approach is called micronavigation, since it aims to capture the problem of mobile robot navigation in its entirety but with a minimalist approach.

RANDOM WALK THEORY APPLIED TO DAPHNIA MOTION

The zooplankton Daphnia or "water flea" — one of the most common crustacean to be found in freshwater — is subject to recent studies. It is known to perform vortex motions under certain light conditions as well as more complex navigational tasks. Experimental data show that Daphnia move with a preferred turning angle, what is of main interest in this paper. The above-mentioned experimental fact is taken in order to derive a diffusion law for these types of motion. Deviations from the free diffusive behavior are investigated, based on random walk theory.