M
M.W.M.G. Dissanayake
Researcher at University of Sydney
Publications - 17
Citations - 3121
M.W.M.G. Dissanayake is an academic researcher from University of Sydney. The author has contributed to research in topics: Kalman filter & Inverse dynamics. The author has an hindex of 7, co-authored 17 publications receiving 2968 citations.
Papers
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Journal ArticleDOI
A solution to the simultaneous localization and map building (SLAM) problem
TL;DR: The paper proves that a solution to the SLAM problem is indeed possible and discusses a number of key issues raised by the solution including suboptimal map-building algorithms and map management.
Book ChapterDOI
An Experimental and Theoretical Investigation into Simultaneous Localisation and Map Building
TL;DR: This paper proves that a solution to the SLAM problem is indeed possible and shows that it is possible for an autonomous vehicle to start in anunknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and simultaneously to compute a bounded estimate of vehicle location.
Proceedings ArticleDOI
Autonomous underground navigation of an LHD using a combined ICP-EKF approach
TL;DR: A new approach for the autonomous navigation of a load-haul-dump (LHD) truck in an underground mine is presented which is found to be robust with respect to occlusions and outliers, demonstrating the successful navigation of the LHD.
Journal ArticleDOI
Automated polishing of an unknown three-dimensional surface
TL;DR: In this paper, an automatic system for polishing an unknown 3D surface using a passively compliant end-effector mounted on the wrist of an industrial robot is described, where a personal computer is used to acquire sensory data, compute the desired configuration of the robot wrist, and to control the robot in a point-to-point mode.
Journal ArticleDOI
A neural network-based method for time-optimal trajectory planning
Gu Fang,M.W.M.G. Dissanayake +1 more
TL;DR: A neural network based algorithm for tim e-optimal trajectory planning is introduced that utilises neural networks for representing the inverse dynamics of the robot.