Author name cluster

N. Bains

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

IROS Conference 1994 Conference Paper

ARK: autonomous mobile robot for an industrial environment

Michael Jenkin
N. Bains
J. Bruce
T. Campbell
Brian Down
Piotr Jasiobedzki
Allan D. Jepson
B. Majarais

This paper describes research on the ARK (Autonomous Mobile Robot in a Known Environment) project. The technical objective of the project is to build a robot that can navigate and carry out survey/inspection tasks in a complex but known industrial environment. Rather than altering the robots environment by adding easily identifiable beacons the robot relies on naturally occurring objects to use as visual landmarks for navigation. The robot is equipped with various sensors that are used to detect unmapped obstacles, landmarks and objects. This paper describes the robot's industrial environment, it's control architecture, and some results in processing the robot's range and vision sensor data for navigation. >

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IROS Conference 1993 Conference Paper

Global navigation for ARK

Michael Jenkin
Evangelos E. Milios
Piotr Jasiobedzki
N. Bains
K. Tran

ARK (Autonomous Robot for a Known environment), is a visually-guided mobile robot which is being constructed as part of the Precarn project in mobile robotics. ARK operates in a previously mapped environment and navigates with respect to visual landmarks that have been previously located. While the robot moves, it utilizes an active vision sensor to register the robot with respect to these landmarks. As the landmarks may be scarce in certain regions of its environment, ARK plans paths which minimize both path length and path uncertainty. The global path planner assumes that the robot will use a Kalman filter to integrate landmark information with odometry data to correct path deviations as the robot moves, and then uses this information to choose a path which reduces the expected path deviation.

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