12,393 research outputs found
Learning Augmented, Multi-Robot Long-Horizon Navigation in Partially Mapped Environments
We present a novel approach for efficient and reliable goal-directed
long-horizon navigation for a multi-robot team in a structured, unknown
environment by predicting statistics of unknown space. Building on recent work
in learning-augmented model based planning under uncertainty, we introduce a
high-level state and action abstraction that lets us approximate the
challenging Dec-POMDP into a tractable stochastic MDP. Our Multi-Robot Learning
over Subgoals Planner (MR-LSP) guides agents towards coordinated exploration of
regions more likely to reach the unseen goal. We demonstrate improvement in
cost against other multi-robot strategies; in simulated office-like
environments, we show that our approach saves 13.29% (2 robot) and 4.6% (3
robot) average cost versus standard non-learned optimistic planning and a
learning-informed baseline.Comment: 7 pages, 7 figures, ICRA202
Robot Navigation in Unseen Spaces using an Abstract Map
Human navigation in built environments depends on symbolic spatial
information which has unrealised potential to enhance robot navigation
capabilities. Information sources such as labels, signs, maps, planners, spoken
directions, and navigational gestures communicate a wealth of spatial
information to the navigators of built environments; a wealth of information
that robots typically ignore. We present a robot navigation system that uses
the same symbolic spatial information employed by humans to purposefully
navigate in unseen built environments with a level of performance comparable to
humans. The navigation system uses a novel data structure called the abstract
map to imagine malleable spatial models for unseen spaces from spatial symbols.
Sensorimotor perceptions from a robot are then employed to provide purposeful
navigation to symbolic goal locations in the unseen environment. We show how a
dynamic system can be used to create malleable spatial models for the abstract
map, and provide an open source implementation to encourage future work in the
area of symbolic navigation. Symbolic navigation performance of humans and a
robot is evaluated in a real-world built environment. The paper concludes with
a qualitative analysis of human navigation strategies, providing further
insights into how the symbolic navigation capabilities of robots in unseen
built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and
Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see
https://btalb.github.io/abstract_map/ for access to softwar
Simultaneous localization and map-building using active vision
An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable long-term localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment.Published versio
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments
Working memory, map learning, and spatial orientation: The effects of gender and encoding interference on the acquisition of survey knowledge
The present experiment investigated the effects of gender and encoding interference on the retrieval of spatial knowledge in a group of 24 male and 29 female students aged 18 to 43 (M = 23.33; SD = 5.78), with 12 to 20 years of education (M = 14.33; SD = 1.76). Each participant was tested individually on their ability to study a map containing 14 labelled landmarks in 1 of 3 interference conditions (i.e., no interference, articulatory suppression, and spatial interference). Then, the participant was blindfolded and asked to point to different aspects of the environment, varying in degrees of familiarity. Specifically, they were asked to indicate the orientation of 4 familiar cardinal directions (over-learned), 4 obscure cardinal directions (intermediate), and 10 landmarks (novel); the latter were cued verbally or visually. Response latency and accuracy were measured. Mixed ANOVAs were conducted with gender (2) and interference (3) as between-subjects factors and cue modality (2) or level of exposure (3) to the environment as within-subjects factors. The results revealed a marked decrease in orientation error and response latency with increasing degrees of familiarity (exposure). In addition, landmarks cued verbally yielded faster and more accurate responses than landmarks cued visually. Also, the presence of any encoding interference during the map study phase resulted in lower accuracy (higher error), especially in the recall of novel information. Lastly, verbal interference affected the accuracy of females to orient to landmarks more than males and the spatial interference yielded the opposite pattern. The findings are discussed in terms of models of working memory, spatial cognition, and gender differences.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M37. Source: Masters Abstracts International, Volume: 44-03, page: 1521. Thesis (M.A.)--University of Windsor (Canada), 2005
Active Mapping and Robot Exploration: A Survey
Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government
Encoding natural movement as an agent-based system: an investigation into human pedestrian behaviour in the built environment
Gibson's ecological theory of perception has received considerable attention within psychology literature, as well as in computer vision and robotics. However, few have applied Gibson's approach to agent-based models of human movement, because the ecological theory requires that individuals have a vision-based mental model of the world, and for large numbers of agents this becomes extremely expensive computationally. Thus, within current pedestrian models, path evaluation is based on calibration from observed data or on sophisticated but deterministic route-choice mechanisms; there is little open-ended behavioural modelling of human-movement patterns. One solution which allows individuals rapid concurrent access to the visual information within an environment is an 'exosomatic visual architecture" where the connections between mutually visible locations within a configuration are prestored in a lookup table. Here we demonstrate that, with the aid of an exosomatic visual architecture, it is possible to develop behavioural models in which movement rules originating from Gibson's principle of affordance are utilised. We apply large numbers of agents programmed with these rules to a built-environment example and show that, by varying parameters such as destination selection, field of view, and steps taken between decision points, it is possible to generate aggregate movement levels very similar to those found in an actual building context
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