Project Title:
Foveal Machine Vision for Mobile Robots using Agent Based Gaze
Amherst Systems Incorporated
30 Wilson Road
Buffalo New York 14221
93-1 05.02 0610 __ AMOUNT REQUESTED $
Foveal Machine Vision for Mobile Robots using Agent Based Gaze
Control
Abstract:
A key robot visual system resource is acuity, and yet this is fixed
in conventional uniform acuity machine vision; regions with little
or no relevance to the robot's mission are sampled at the same
resolution as key features, occupying valuable signal bandwidth and
computational resources, and increasing system latencies. Foveal
active vision features imaging sensors and signal processing with
graded acuity, and context sensitive sensor gaze control, analogous
to that prevalent throughout vertebrate vision. Foveal systems
operate more efficiently because resolution is treated as a
dynamically allocatable resource. Wide field-of-view, localized
high acuity, and fast frame rates are achieved while minimizing
irrelevant sensor data. The maturation of foveal technology is
hampered by the need for fast and intelligent gaze control. This
program will prototype a mobile robot foveal vision system which
relies on an agent based grounded layered architecture with
integrated reasoning for gaze control. This architecture will
facilitate the development of successful active vision behaviors
(sensor pointing and platform motion) required for operation in a
"dynamic space environment". Learning techniques will be
investigated which accelerate attentive active vision behaviors by
evolving them into preattentive responses.
Foveal machine vision offers improved performance, greater platform
intelligence and autonomous operation, and lower system cost than
traditional uniform acuity sensors. It is intended for applications
in poorly controlled environments where response time is critical,
including autonomously guided vehicles and mobile robots, non-
emissive guidance and collision alert, automated transport in
dynamic environments, and assembly and inspection.
Foveal Vision, Autonomous Agents, Attention, Learning, Agent
Architecture, Hierarchical Processing, Multiresolution, Gaze
Control