Navigation In Large Environments Research Articles

NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation

Spatial navigation depends on the combination of multiple sensory cues from idiothetic and allothetic sources. The computational mechanisms of mammalian brains in integrating different sensory modalities under uncertainty for navigation is enlightening for robot navigation. We propose a Bayesian attractor network model to integrate visual and vestibular inputs inspired by the spatial memory systems of mammalian brains. In the model, the pose of the robot is encoded separately by two sub-networks, namely head direction network for angle representation and grid cell network for position representation, using similar neural codes of head direction cells and grid cells observed in mammalian brains. The neural codes in each of the sub-networks are updated in a Bayesian manner by a population of integrator cells for vestibular cue integration, as well as a population of calibration cells for visual cue calibration. The conflict between vestibular cue and visual cue is resolved by the competitive dynamics between the two populations. The model, implemented on a monocular visual simultaneous localization and mapping (SLAM) system, termed NeuroBayesSLAM, successfully builds semi-metric topological maps and self-localizes in outdoor and indoor environments of difference characteristics, achieving comparable performance as previous neurobiologically inspired navigation systems but with much less computation complexity. The proposed multisensory integration method constitutes a concise yet robust and biologically plausible method for robot navigation in large environments. The model provides a viable Bayesian mechanism for multisensory integration that may pertain to other neural subsystems beyond spatial cognition.

Robot navigation in the real world:: Experiments with Manchester’s FortyTwo in unmodified, large environments

Mobile robot navigation under controlled laboratory conditions is, by now, state of the art and reliably achievable. To transfer navigation mechanisms used in such small-scale environments to applications in untreated, large environments, however, is not trivial, and typically requires modifications to the original navigation mechanism: scaling up is hard. In this paper, we discuss the difficulties of mobile robot navigation in general, the various options to achieve navigation in large environments, and experiments with Manchester’s FortyTwo, which investigate how scaling up of navigational competencies can be achieved. We were particularly interested in autonomous mobile robot navigation in unmodified, large and varied environments, without the aid of pre-installed maps or supplied CAD models of the environment. This paper presents a general approach to achieve this. FortyTwo regularly travels the corridors of the Department of Computer Science at Manchester University, using topological maps, landmarks, low level “enabling behaviours” and active exploitation of features of the environment. Experimental results obtained in these environments are given in this paper.

Topological direction-giving and visual navigation in large environments

In this paper, we propose and investigate a new model for robot navigation in large unstructured environments. Current models, which depend on metric information, have to deal with inherent mechanical and sensory errors. Instead we supply the navigator with qualitative information. Our model consists of two parts, a map-maker and a navigator. Given a source and a goal, the mapmaker derives a navigational path based on the topological relationships between landmarks. A navigational path is generated as a combination of “parkway” and “trajectory” paths, both of which are abstractions of the real world into topological data structures. Traversing within a parkway enables the navigator to follow landmarks that are continuously visible. Traversing on a trajectory enables the navigator to move reliably into featureless space, based on local headings formed by visible landmarks that are robust to positional and orientational errors. Reliability measures of parkway and trajectory traversals are defined by appropriate error models that account for the sensory errors of the navigator, the population of neighboring objects, and the rotational and translational errors of the navigator. The optimal path is further abstracted into a “custom map”, which consists of a list of symbolic directional instructions, the vocabulary of which is defined by our environmental description language. Based on the custom map generated by the map-maker, the navigating robot looks for events that are characterized by spatial properties of the environment. The map-maker and the navigator are implemented using two cameras, an IBM 7575 robot arm, and a PIPE (Pipelined Image Processing Engine.)