M.W.M.G. Dissanayake

A solution to the simultaneous localization and map building (SLAM) problem

The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from estimation-theoretic foundations of this problem, the paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. The paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, the paper discusses a number of key issues raised by the solution to the SLAM problem including suboptimal map-building algorithms and map management.

An Experimental and Theoretical Investigation into Simultaneous Localisation and Map Building

The simultaneous localisation and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [5, 4, 2], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown 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.

Automated polishing of an unknown three-dimensional surface

An automatic system for polishing an unknown three-dimensional surface using a passively compliant end-effector mounted on the wrist of an industrial robot is described. As polishing proceeds, the end-effector uses position sensors to measure the misalignment of the robots wrist from the local surface normal. A personal computer is used to acquire sensory data, to compute the desired configuration of the robot wrist, and to control the robot in a point-to-point mode. Low bandwidth point-to-point control is possible because of the passive compliant movement of the end-effector. Contact with the work surface can be maintained within an angular range of ±8° and a ±10 mm range of normal translational movement. Experimental performance tests show that the polishing system can function well under a variety of working conditions.

Towards automatic container handling cranes

This paper describes the design and implementation of a semi-autonomous, and ultimately fully autonomous, container quay-crane. The new crane is based on a novel reeving arrangement which allows both fast and accurate gross motion as well as fine micropositioning. The paper describes the essential theory behind this design and presents experimental results from a 1/15th scale model. The proposed instrumentation of this crane is also briefly described as are key elements of the operator interface.

INS-based identification of quay-crane spreader yaw

A crucial problem in crane control is to identify exactly the position and orientation of the load in space. This paper describes a new non-contact method for determining the crane load location by means of an inertial navigation system (INS) and a Kalman filter. The Kalman filter estimates the spreader position, which is not observed by the INS. Experiments were conducted on a 1/15th geometric scale model of a quay-crane. The work has potential application in the development of integrated estimation and control systems for full-scale quay-cranes.

Control of Load Sway in Enhanced Container Handling Cranes

This paper describes the design, implementation and control of an enhanced container quay-crane. The new crane is based on a novel reeving arrangement which allows both fast and accurate gross motion as well as fine micropositioning. The paper describes the essential theory behind the mechanical design and the design of a controller that minimises load sway. The controller uses measurements of the rope tensions to provide artificial damping of the load. Experimental results from a 1/15th scale model are presented. The control strategy was found to be extremely effective, damping spreader oscillations within two or three cycles.

General Optimiser for Continuous Inverse Analysis

While a number of optimisation methods have been proposed and used for inverse analysis, we do not know the best optimisation method for each inverse problem as the solution of inverse problems is not known in nature. This paper presents a general optimiser, in which one can implement a variety of optimisation methods. With this optimiser, one can not only implement existing optimisation methods but also develop a new optimisation method easily, which include both single-objective and multi-objective optimisation methods. Thanks to the architecture of the optimiser, a multi-objective gradient-based optimisation, which was difficult to formulate, has been proposed. The availability of the optimiser has been confirmed by implementing various optimisation methods.

Identification of Load Sway in Quay-Cranes

This paper describes the use of an inertial navigation system in conjunction with a Kaiman filter to estimate crane load sway. The estimator was based on a simple dynamic model of the crane yaw motion. Experiments were conducted on a l/15 th geometric scale model of a quay-crane. The performance of the filter was assessed using data obtained from video images. A yaw position estimator was able to track accurately the motion of the load over extended time periods.