A Primate-Inspired Autonomous Navigation Algorithm Using the Cognitive Mechanism of Mental Rotation

Pettinati, Michael J.; Arkin, Ronald C.

doi:10.1007/978-3-319-11900-7_33

Cited by 1 publication

(4 citation statements)

References 17 publications

(26 reference statements)

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“…The feedforward step as described in [4] first computes the occupancy grid from the depth images generated by the Microsoft Kinect for both the current and the goal images. A number of mental transformations are then applied to the current occupancy grid in order to match it with the occupancy grid generated from the goal image.…”

Section: Navigation Algorithmmentioning

confidence: 99%

“…The navigation algorithm described in [4], implements segment-based navigation. The segmentation algorithm developed by Natesh et al is described in [19].…”

Section: Segmentation-based Advice-givingmentioning

confidence: 99%

“…Arkin et al in [2] and [3], discuss the architecture of a system that uses mental rotation in robot navigation using local depth maps generated at the current location of the agent as it navigates. Pettinati et al in [4] describe the implementation of such a navigational algorithm which uses a series of mental transformations on the generated local depth maps in order to compute a rotational and translational vector that directs the agent to a goal location. The results obtained confirm the usefulness of mental rotation in robot navigation.…”

Section: Introductionmentioning

confidence: 99%

“…In this project, we attempt to explain the possible correlation between these two processes by unifying advice-giving and mental rotation into a navigational algorithm implemented on a robotic agent. The work in this paper builds upon the existing framework developed in our lab ( [2], [3], [4]), in order to incorporate advice-giving.…”

Section: Introductionmentioning

confidence: 99%

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Primate-inspired autonomous navigation using mental rotation and advice-giving

Velayudhan

Arkin

2015

2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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The cognitive process that enables many primate species to efficiently traverse their environment has been a subject of numerous studies. Mental rotation is hypothesized to be one such process. The evolutionary causes for dominance in primates of mental rotation over its counterpart, rotational invariance, is still not conclusively understood. Advice-giving offers a possible explanation for this dominance in more evolved primate species such as humans. This project aims at exploring the relationship between advice-giving and mental rotation by designing a system that combines the two processes in order to achieve successful navigation to a goal location. Two approaches to visual advice-giving were explored namely, segment based and object based advice-giving. The results obtained upon execution of the navigation algorithm on a Pioneer 2-DX robotic platform offers evidence regarding a linkage between advicegiving and mental rotation. An overall navigational accuracy of 90.9% and 71.43% were obtained respectively for the segmentbased and object-based methods. These results also indicate how the two processes can function together in order to accomplish a navigational task in the absence of any external aid, as is the case with primates.

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Section: Navigation Algorithmmentioning

confidence: 99%

“…The navigation algorithm described in [4], implements segment-based navigation. The segmentation algorithm developed by Natesh et al is described in [19].…”

Section: Segmentation-based Advice-givingmentioning

confidence: 99%