Autonomous Robotic Systems - Anibal T. de Almeida and Oussama Khatib (Eds) Part 10 pot

Considerable work within the robotics and computer vision is highly appli- cable to real-world automation needs such as exist within the mining industry.. Gurgenci, "Automatic control of

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Gdzzly

Figure 10 Results from field trip The diagram in the top left hand corner shows the experimental setup Note that this setup caused range shadows behind large rocks

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Figure 11 The view from above (top), and from the front (bottom) of the grizzly covered in large rocks

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Figure 12 Results from the laser scanner looking at a charging face Note the enlargement of the front view clearly shows a ring of drilled holes and a butt

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ideal location of the laser scanner would have been directly above the grizzly, eliminating most shadows, but this was not possible given the limited a m o u n t

of time on site Figure 11 shows in more detail the data collected when some large rocks were d u m p e d on the grizzly The lower portion of the figure shows the scene from the front

Figure 12 shows some different views of the end of a mine tunnel, all derived from one scan The figure clearly shows details such as the drilled blast holes and a blasting ' b u t t ' (basically a hole in the rock face caused from the previous blasting cycle) T h e floor appears extremely smooth because it was a pool of water

3.2 A s e m l - a u t o m a t e d r o c k b r e a k e r

Dealing with oversize rocks is a common problem in surface and underground mining Large rocks may j a m a crushing plant or chute, or be too large to travel along a conveyor system In underground mines grizzly screens are used

to filter out oversize rocks A grizzly screen is typically a very solid steel structure over an ore pass or crusher that provides a mesh size in the range 0.5 to l m Material m a y reach the grizzly from an ore pass via a chain feeder

or be d u m p e d directly by truck The grizzly screens must be kept clear from build up of loose material or oversize rocks A rock-breaker, see Figure 6, is a manually controlled hydraulic arm that carries a hydraulic impact hammer In structure it is akin to a back-hoe excavator, and kinematically it is similar to

a standard industrial robot

In order to a u t o m a t e this process we would need

1 a computer controllable rock breaking boom,

2 a 3-D sensing system,

3 the a u t o m a t i o n system, and

4 a teleoperation system

The proposed system would use 3-dimensional sensors to monitor the griz- zly, and when necessary control the breaking boom so as to clear the griz- zly The imaging aspects of rock-breaker a u t o m a t i o n have been previously studied[14, 15] and trialled in the laboratory using a small scale model [16] Due to the difficulty, in what is a complex and only partially structured environment, of foreseeing all eventualities 2 we do not believe that at this stage

it is feasible to fully a u t o m a t e the process Complications involved in carrying out this task include dealing with foreign objects such as timber props and ground support bolts In the event of the system being unable to autonomously clear the grizzly, it would signal a remote operator who would use teleoperation

of the breaker to complete the task Such limited human intervention would

be the most cost effective solution for dealing with these situations, and would make it possible for a single operator to supervise several rock breakers located

at different sites around the mine

2Rock shapes and type, and foreign objects

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.3-Dsensor

Computer controllable rock b r e a k ~

Grizzly

\

Automation system

system

Figure 13 The proposed semi-automated rock breaker

4 C o n c l u s i o n

The ultimate aim of mine automation will be to remove miners from the haz- ardous areas of the mining environment where the work will instead be per- formed by autonomous and sensate mining machines Such a vision is many decades from reality and in the interim we can hope to make small steps to- ward this goal One step is to increase the productivity of existing mining equipment by assisting the operator so that one operator can supervise several machines The dragline and the rock breaker automation projects described here are example of this

Considerable work within the robotics and computer vision is highly appli- cable to real-world automation needs such as exist within the mining industry The research community has demonstrated the feasibility of using machine vi- sion to 'close the loop' on the position of robot manipulators Such technology could be usefully applied to many applications in mining Superficially these may seem very different problems, but this is largely a matter of scale Larger machines in fact require reaction times that are considerably longer than those being demonstrated now in robotics laboratories The biggest, but not insur- mountable, challenge is the complexity of scene analysis in a complex mining environment

Acknowledgements

The authors gratefully acknowledge the help of their colleagues Stuart Wolfe, David Hainsworth, Stephen Nothdurft, Zheng-De Li, Jasmine Banks, Hal Gur- genci, Don Flynn, Peter Nicolay, Allan Boughen and Daniel Sweatman The dragline project is funded by a consortium consisting of the Australian Coal Association Research Programme (as project C5003), Pacific Coal Pty Ltd, BHP Australia Coal Pty Ltd and the Cooperative Research Centre for Mining Technology and Equipment (CMTE), a joint venture between AMIRA, CSIRO

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and the University of Queensland Bucyrus Erie Australia and Tritronics Pty Ltd have provided valuable in-kind support, and Tarong Coal have gener- ously allowed the a u t o m a t e d swing system to be installed on their BE1370 The underground mining robotics work has been supported by the C M T E and AMIRA project P440 which was sponsored by Mount Isa Mines, N o r m a n d y Poseidon and Western Mining

R e f e r e n c e s

[1] P I Corke, Visual Control of Robots: High-Performance visual servoing Mecha-

tronics, Research Studies Press (John Wiley), 1996

[2] S Hutchinson, G Hager, and P Corke, "A tutorial on visual servo control,"

IEEE Transactions on Robotics and Automation, vol 12, pp 651-670, Oct

1996

[3] D Hainsworth, G Winstanley, Y Li, P Corke, and H Gurgenci, "Automatic control of dragline operation using machine vision control of bucket position,"

in Proc First CMTE Annual Conference, (Brisbane), pp 111-114, July 1994

[4] P I Corke, G Winstardey, and J Roberts, "Dragfine modelling and control," in

Proe IEEE Int Conf Robotics and Automation, (Albuquerque, NM), pp 1657-

1662, 1997

[5] G Winstanley, P Corke, and J Roberts, "Dragfine swing automation," in Proc

IEEE Int Conf Robotics and Automation, (Albuquerque, NM), pp 1827-1832,

1997

[6] Z Li, P Corke, and H Gurgenci, "Modelling and simulation of an electro-

hydraulic mining manipulator," in Proc IEEE Int Conf Robotics and Automa-

tion, (Albuquerque, NM), pp 1663-1668, 1997

[7] R A Jarvis, "A perspective on range finding techniques for computer vision,"

IEEE Transactions on Pattern Analysis and Machine Intelligence, vol PAMI-5,

pp 122-139, Mar 1983

[8] R Chatila, S Fleury, and M Herrb, "Autonomous navigation in natural envi-

ronments," in Experimental Robotics III (T Yoshikawa and F Miyazaki, eds.),

pp 425-443, Springer-Verlag, 1994

[9] M Thompson, ed., Manual of Photogrammetry Falls Church, VA: American

Society of Photogrammetry, 3 ed., 1966

[10] P Fua~ "A parallel stereo algorithm that produces dense depth maps and pre-

serves image features," Machine Vision and Applications, vol 6~ pp 35-49~ 1993

[11] R Zabih and J WoodfiU, "Non-parametric local transforms for computing visual

correspondence," in Proc 3rd European Conf Computer Vision, (Stockholm),

May 1994

[12] P Dunn and P Corke, "Real-time stereopsis using FPGAs," in Proc Intl Work-

shop on Field Programmable Logic,, (Imperial College, London), Sept 1997 [13] J Woodfill and B V Herzen, "Real-time stereo vision on the parts reconfig-

urable computer," in IEEE Workshop on FPGAs for Custom Computing Ma-

chines, pp 242-250, Apr 1997

[14] S Elgazzar, J Domey, P Boulanger, and G Roth, "Three-dimensional imaging

for mining automation," in Proe 5th Canadian Syrup on Mining Automation,

pp 334-340, 1992

[15] J Domey and M Rioux, "3-d vision sensors and their potential - - applications

in mining automation," in 3rd Canadian Syrup Mining Automation, (Montreal),

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pp 187-193, Sept 1988

[16] C Cheung, W Ferrie, R Dimitrakopoulos, and G Carayanis, "Towards com-

puter vision driven rock modelling," in Proc 2nd Canadian Conf on Computer

Applications in the Mineral Industry, (Vancover~ B.C.), Sept 1991

HelpMate@, The Trackless Robotic Courier:

A Perspective on the Development of a Commercial Autonomous Mobile Robot

John M Evans

HelpMate Robotics, Inc

Danbury, CT, USA

e v a n s @ h e l p m a t e , c o m

Bala Krishnamurthy

HelpMate Robotics, Inc

Danbury, CT, USA

b a l a @ h e l p m a t e c o m

Abstract

HelpMate Robotics has developed an autonomous mobile robot courier for material transport in hospitals These machines are in operation around the world,

operating in uncontrolled and unsupervised environments up to 24 hours per day The history and technology of the HelpMate robot are presented in this paper with the intention of providing a real world perspective on transitionmg technology from the laboratory to the marketplace

1 I n t r o d u c t i o n

HelpMate Robotics Inc (HRI), of Danbury, Connecticut, USA, has developed a hospital courier robot that is the benchmark of commercial success in the emerging field of service robots This paper will explore the decade of development of the HelpMate robot, focusing on the evolution of the technology from laboratory exploration to hardened commercial product

In trying to define market opportunities for robots in service applications and to match enabling technologies against those opportunities, several possibilities originally stood out: floor cleaning, security, hazardous enviromnents and hospital materials transport All required autonomous mobile robots navigating in indoor structured environments It was this technology base and these markets on which

we focused early attention

1.1 The Hospital as a Target Market

Health care is an obvious market for automation because of the high and rising costs that must be tamed if any attempt at universally available care is to be offered

in our society Cost containment is an overriding theme in health care

183

management, and robotics is one technology to reduce labor costs Operation o f hospitals around the clock on a multi-shift basis makes capital justification easier,

so this was a natural target

Development o f the HelpMate concept has been described in earlier papers

[1,2,3,4,5] T h e final machine, shown in Figure 1, is able to navigate in crowded hallways, avoiding people and inanimate obstacles as it encounters them, and using the walls o f the hallways as the principal navigation reference Figure 2 shows the applications for HelpMate

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Figure 1: HelpMate Robot Courier

184

Dietary: late and special request trays Supply: equipment and material Lab: speciman and sample transport Pharmacy: medication and supplies Med Records: patient files

Administration: mail and reports Radiology: films

Mail: mail and packages

Figure 2: Applications of HelpMate

2 N a v i g a t i o n : a p r o b l e m in S e n s i n g

Over the years, we have come to believe that the key to autonomous mobile robot navigation is sensory perception, ffyou can obtain a good, dynamic, high

resolution picture of the world around the robot, then you can successfully use any

of several algorithmic approaches to planning a collision free path through the environment

Much of the early work on navigation algorithms hypothesized known, static worlds, what we would call "blocks worlds" after the highly structured and artificial worlds used in early machine vision research [6] The heritage is that of trajectory planning for robot arms in free space, with force or compliance control considered as a special case [7] Work is still presented today on such ideas Potential field models, clothoids, path planning for non-holonomic vehicles: all presume a totally known and static environment [8,9,10]

But the real world is not static Hospital hallways, in particular, can be filled with moving people: employees, patients and visitors So the problem becomes one of dynamic sensing, and control strategies must be dynamic and reactive

Further, it turns out that there are many "stealth" objects in the real world for any single sensory modality Sound waves bounce off any hard, flat, angled surface and are absorbed by soft material such as a blanket Light bounces off mirrors and chromed surfaces in a specular fashion and passes through glass, and light is absorbed by flat black material such as black wool pants Hence, a multiplicity of different types of sensors maximizes the probability of sensing objects prior to collision Contact sensors to detect collision are always required

The HelpMate robot uses sonar, vision, and contact sensors to interact safely with people and obstacles in a hospital world [3]

This combination of sensors is not unique and was in fact already known in the research community before we started working on HelpMate During the early and