Autonomous Battery Recharging for Indoor Mobile Robots

Seungjun Oh

Australian National UniversityDepartment of Engineering

Canberra, ACT 0200Australia

Alexander Zelinsky

Australian National UniversityResearch School of Information

Sciences and EngineeringCanberra, ACT 0200

Australia

http://syseng.anu.edu.au/rsl/

Ken Taylor

Australian National UniversityResearch School of Information

Sciences and EngineeringCanberra, ACT 0200

Australia

Abstract

This paper describes a method forautomatically recharging the batteries on amobile robot. The robot used in this project isthe Nomadic .Technologies™ Nomad XR4000mobile robot. The battery recharging systemwas implemented using the robot's built-insensors to control docking with a simplerecharging station. The recharging station hasan AC power plug, an infra-red beacon and atarget designed for detection by the Sick LaserRange Finder. The robot's IR sensors performthe beacon detection. The robot moves towardsthe beacon until the. laser target pattern isvisible to the Sick laser. The system iscurrently under development and undergoing itsperformance testing.

1 Introduction

A mobile robot is being prepared at the AustralianNational University for teleoperation using a Web­browser from the Internet. It is intended that therobot will be available to operators continuously withonly occasional local intervention required. With abattery life of approximately 6 hours the requirementfor occasional intervention can only be achieved ifthe robot is provided with a method for automaticrecharging. This paper describes the development ofthe required automatic recharging system.

1.1 Background

In the late 1940's, Grey Walter developed thefirst autonomous recharging mobile robots, named"Tortoises" [Walter, 1953]. These robots had lightfollowing behaviour that is used for his neurologicalresearch. An important feature of these robots wasautonomous recharging. Walter had created arecharging hut that contains a light beacon and abattery recharger that makes contact with the robotwhen the robot enters the.hut.

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More recently,.Universityof Tsukuba havebeen implementing an ··autonomous rechargingmobile robot, namedYamabico-Liv [Yuta, 1998].Its robust navigation system that includes erroradjustment and environment map, guides the robotinto its recharging station. The robot has specialhardware that enables the electric contact to thebattery recharger.

Another tour-guide robot was developed at theRobotics Institute of Carnegie Mellon University.The robot named Sage is a modified NomadXR4000 which provides tours around the CarnegieMuseum of Natq.ral History [Nourbakhsh, 1998].It is also capable of autonomous recharging byusing a CCD camera and a 3-D landmark placed inthe environment. The landmark is located directly'above the plug and it guides the robot's positionfor reliable docking.

1.2 Concept

An example of navigation used for our systemis similar to an aircraft landing. When the aircraft·approaches an airport for landing, it does not beginto align to the runway until it enters the primarycontrol zone nearby the airport. Within the zone,the air traffic controller will nominate a runwayand guide the aircraft direction. for alignment withthe runway. A similar concept is applied to robotrecharge docking. The •robot approaches therecharging station and begins to align itself to theAC power plug when the alignment guidance isvisible. Therefore, the solution to this project isdivided into two sections, long distance approach,and close approach for an accurate alignment.

1.3 Robot

Although there are many possible solutionstowards autonomous recharging of a robot, theconstraint of minimum hardware modification tothe robot restricts the range of solutions. For amobile robot that is capable of autonomousrecharging must have following hardwarecharacteristics:

1.4 Sensors

Figure 3: The recharging station

1.5 Recharging Station

The recharging station has targets for robot'ssensors and the power plug. Using the propertiesof the sensors, targets were designed to create aunique landmark. The recharging station, asshown in Figure 3, consists of an IR beacon for theIR sensors, and a "Grid" as a target for the Sicklaser. Software was developed for detection ofthese targets. The algorithm consists of detectionof the target and moving' towards 'the target toposition the robot at the required accuracy of±lmm before the plug insertion.

1.6 Paper Outline

In this paper,. Section 2 discusses therelationship between the sensors and their targetdesigns. The details of the short-range targetalignment, long-range beacon detection and theirperformances are presented in Section 3, followedby concluding remarks.

A generic lEe power plug holder wasdesigned to provide flexibility in the plug insertion,reduce in sparking, and compliance for theaccuracy required. It is also designed to adjustplug height, and to fit varieties of plug designs.

The hardware for the recharging station costsunder $300 and is designed for ease ofmanufacturing, with readily availa~lematerials.

Other compliance mechanisms or electricalcontacts were considered. However, it is requiredto be well-insulated for the safety, since 240VAC

. is used for the recharging.

SickLaser

.Range-Finder

.BumperIR.Sonar

• An on-board battery recharger if it is powered byrechargeable batteries. Alternatively, batteryconnectors those link to a stationary recharger ata recharging station;

• ability to move with accuracy;• and sensors to guide its position with accuracy.

Nomadic Technologies™ Nomad XR4000 mobilerobot has met the criteria for self-recharging. .It hasan on-board battery recharge to support four heavy­duty 12VDC lead-acid batteries, hardware controlledholonomic-drive system, and a number of differentsensors for navigational applications. It. weighs150kg, diameter of 62cm and height of 85cm. Theplug used in the recharging is the standard IEC plug.The AC socket for the Nomad XR4000 robot hadbeen moved directly below the Sick laser range finderto simplify the positioning problem.

Figure 1: The Nomad XR4000 indoor mobile robot

The sensors used are the 48 short-range infra-redproximity sensors surrounding the robot, and a SickLMS-200 laser range finder fitted as a manufacturersoption in the body of the robot with a 1800 viewingangle, as shown.in Figure 2.

Figure 2: Nomad's Door-mounted - Bumper, IR and Sonarsensors, and the Sick LMS-200 laser range finder.

2 Long Range Approach

It was found that the IR sensors can detectchange in infra-red lights up to 7m away, hence theywere used for long distance beacon detection. TheSick laser range finder has range up to 120m at thelowest resolution. However, for our applicationshorter range of 8m with high resolution of 0.5 0 inangle and 1mm in distance, was used as the short­range alignment sensor.

The Nomad's built-in IR sensors are used to detectthe IR navigational beacon. A long-rangenavigational beacon is designed to exploit the IRsensor characteristics.

2.1 Long Range Beacon

The IR sensors on the Nomad are used to senseproximity within the range of approximately 0.5m.The robot emits IR radiation that is reflected from

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nearby objects. The IR sensor detects change in IRintensity. A value of 0 to 255 is returned dependingon the intensity of the IR signal detected. While it isnormally used as a short-range proximity detector, wefound that by using reflective tape, IR radiation isreflected at greater distances up to 5m.

The detection can be achieved at a longerdistance of 7 metres using IR spot-lights that are usedfor CCD camera's for night vision. An oscillatorcircuit is added to create flashing IR beacon for theIR sensors to detect. The IR spot-lights are arrangedto illuminate the 1800 of its surrounding, as shown inFigure 4.

The distinction between the beacon and nearbyobjects is their proximity that is made by the Sicklaser range finder. The beacon must be sufficientlyfar away from the robot so that the distinction isclear. Therefore the IR beacon cannot be identifiedat a close range of approximately 1m. The obviousassumption is that the sizes of close-by objects aretall enough to return close proximity by the Sicklaser and the IR sensors. Using this method, therobot is able to move within approximately 1mradius away from the recharging station and thusenter the short-range region.

3 Short Range Approach

When the Nomad enters the short-range region, theSick laser range finder will be able to detect thegrid pattern. The grid pattern is designed togenerate a pattern that is distinguishable from therest of the environment. The grid pattern is used toguide the robot align to the power plug prior to theinsertion.

Figure 4: The IOl1g-range IR beacon. It consists of four IR"spot-light" LED arrays and an oscillator circuit. 3.1 Short-Range Target: Grid

2.2 Long-Range IR Beacon Detection

The IR sensors alone cannot distinguish between theIR beacon and nearby objects. The IR detection isperformed using combination of the Sick laser rangefinder, Sonar sensors and the IR sensors, as shown inFigure 5.

Object

IR Proximity Detection

Figure 5: The distinction· between the IR proximitydetection and the IR beacon detection using the Sick laserrange finder and Sonar sensors.

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As the robot approaches the recharging station, theSick LMS-200 laser range finder is used to provide2-dimensional view of the environment with 1800

coverage at 0.5 0 angular and 1 millimetre distanceresolution. To identify the grid from the rest of theenvironment, a distinctive pattern was designed.The target also provides guidance information forthe robot at accuracy of ±lmm position errorperpendicular to the plugging direction. Figure 6shows the Grid target that is used for guiding therobot's position during the docking.

Figure. 6: The Grid is used for position guidance andalignment of the robot using the Sick laser range finder.

The design features of the Grid are:• 4 "wide-enough" strips to generate sufficient data

points.

• 5 gaps to generate 8 edges evenly spaced out.• Constant distance between the back plate and the

Grid slots.

• Wide back plate for oblique angle detection.

• Dull surface to increase diffusion of laser beams formore accurate measurement.

• Two plastic' strips that do not diffuse the laser beam, tomark the start and the end of the Grid.

An algorithm was developed to identify and alignthe robot to the Grid using the geometrical features.Depending on the robot's position, the Sick laserdetects the Grid differently. As the laser beam doesnot diffuse at the shiny plastic surfaces the maximumrange value of 8m is always measured in theirdirection regardless of the robots distance'from them.The algorithm relies on the grid being positioned neara wall so that everything in the Sick laser's view isless than 8 metres away.

Grid

B. Too FarA\\ray

Figure 8: Sick laser data plots of the Grid at differentpositions of the Nomad. It ~s categorised into 4 differentcases.Note: The lines are joined at the Grid edges for clarity.The arrow represents the front of the Nomad.

.lIIIl111U8Sl

3.3 Grid Alignment and Approach

A number of points on the Grid's front surfaceare extracted during the Grid detection procedure.A line is fitted to the points to obtain theorientation of the robot relative to the Grid. Thisallows the robot to move to the position as shownin Figure 8A, by combination of rotation andtranslation.

Once this po~ition is reached, the Sick laser canaccurately extract all the features on the Grid.Another line is fitted along the front surface pointsto correct rotational orientation, and edges aredetected to achieve accurate translational position.

Figure 9 shows the differential plot of the Gridpattern generated from the Sick laser range finder.

D. Too Far LeftlRight

C..Too Close

A. The robot is facing the Grid, at a distance ofapproximately O.5m. This is the desired positionprior to the grid alignment and approach. All gridfeatures are clearly visible.

B. The robot is too far away from the Grid.. Back platepoints between the slots are incorrect. The IRbeacon will "pull" the robot closer. The Gridmaybe detecte€l.

C. The robot is too close to the Grid. Too many pointsare visible at the edges giving curved effect.

D. The robot is too far right to the Grid and is thereforenot in the centre of the field of view.

Figure 7: The grid consists of Gaps and Surfaces.

Gap

Grid Plot

If the conditions are not satisfied, ie, the Grid isnot detected, the algorithm will look for the plasticstrips that are positioned at the start and the end of theGrid. The maximum range of the Sick laser isreturned from the shiny plastic strips. If only oneplastic strip is detected, then the distance to surfaceson the left and right the strip is compared. The closersurface is assumed to be the Grid.

3.2 Short-Range Grid Detection

As the robot enters the short-range region there willbe sufficient Sick laser data points on each slot andstrip of the grid. However, depending on the robot'sposition relative to the Grid, the laser detects the Griddifferently. Figure 8 illustrates how the Grid shape isseen depending on the position of the robot.

By considering the 4 possible cases, a generalmethod was developed to detect the grid. The griddata is broken up into two components, surfaces andgaps, as shown in Figure 7. A gap is the largerdistance difference between two adjacent sets of datapoints. A surface is the sets of points that are closetogether, and they represent the .Grid strips or theback plate. Gaps are used to extract surfaces. Thepattern is recognised by counting the occurrence ofthe surfaces and the difference in distance betweenfront and back surfaces. These conditions must alsooccur within the width of the Grid.

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Nomad Movement Path-

500 1000 1500-2000 -1000 -1000 -roOX (mm)

I TRANSiSTiON POINT

DOCKiNG POINT ill" SHORT-RANGE_

OL......J-.._-'------JI-----L._-l..-_~---I-~'-I--_AP_PL_RO_.A_CH...L._____JI._J-3000 -i!5oo

500

1000

1500

3500

200

-400

600

DKierenHal Ploi

The edges (or gaps) of the Grid can easily be detectedby determining the magnitude of the peaks and itsoccurrence. However, when the robot is positioned atan undesired position, as shown in Figure 8, some ofthe peaks may not be detected due to distortion in theGrid shape. The algorithm was developed to guessthe Grid and roughly position the robot to the desiredposition, shown in Figure 8A.

Io~ ~~~~y~Bi -200o

Figure 10: The Nomad's path to the recharging station.

3.4 Performance

Figure 9: Plot of differential of the Grid pattern with respectto the Sick laser sensor's angle index, showing regularpeaks at the edges of the Grid.

The robot slowly approaches towards the powerplug as it corrects its position using the Grid edgesalong the way until it is ready to dock into the powerplug. It was found that accurate positioning can beperformed by applying a short delay of 1 second inbetween each movement command. The delay alsohelps the Sick laser sensor to scan the environmentmore accurately by taking an average of samples.

Once the robot is docked, it checks the batteryvoltage levels. If the voltages are increased then thedocking is successful, otherwise the robot movesbackwards and retries the Short-range dockingsequence.

Event Distance to AccumulatedDescription Recharging Time

StationStarting 4 rnetres 0

PointLong range --- 58 secondsapproach

Short range 0.5 metres 1 n1inutel\pproachTransition

Short Range --- I minuteApproach 21 seconds

Plug 0 2 nrinutesinsertion 30 seconds

Total

Table 1: Time taken for the Nomad's recharge docking

Figure 11: Video captures of the recharge docking,showing long-range and short-range approach and close­up of the docking.

There were 6 failures occurred out of 30 testsperformed. The failures were mainly due toundetected targets. One of the most commonfailures is due to an undetected IR beacon. Thiscan be caused by reflection of the IR emISSIonfrom other nearby objects. Another common

240lao 200A~lalndex

100140

u--400

-800

-eoo

The robot's path is shown in Figure 10 with the threemain even points. At the starting point, the robotstarts to search for the IR beacon using the IRsensors, Sonar sensors and the Sick laser range finder.Once the beacon is found, the robot approachestowards the beacon, until it reaches the short-rangeregion. Then the robot performs transition from long­range approach mode to short-range approach. Atthis stage, the robot identifies the grid and performsthe alignment. Once aligned the robot approachestowards the grid, and the plug is inserted at thedocking point. The time taken at each event istabulated in Table 1. Figure 11 shows video frame­captures of the docking sequence.

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failure is due to undetected Grid, which can beovercome by moving the robot to the desired position.

4 Conclusion

This paper has introduced how hardware targets forsensors can be generated using the sensor propertiesfor determining the robot's destination. Addition ofmore features to the target, such as the shiny plasticson the Grid, had increased probability of the targetdetection.

Future work will involve replacement of the IRbeacon with a reflective tape. Since the Sick laserrange finder can measure reflectivity at a much longerrange, hence the IR beacon will no longer berequired. The hardware cost of the system will bereduced to approximately $100. Addition ofwandering and searching behaviour is to be added tothe system.

The system is currently under development withfocus on the reliability.

Acknowledgments

Thanks to Dr. Samer Abdallah for his supervision atthe AND Department of Engineering. Also, thanks toMr. Harley Truong for the help with buildinghardware, Mr. Matthew Shaw for coding originallong-range approach and Mr. Colin Thomsen for thehelp with the coding.

References

[Walter, 1953] Walter, W. G., The Living Brain. W.W. Norton, New York, 1953.

[Yuta, 1998] Yuta, S., Hada, Y., Proceedings of the1998 IEEE/RSJ IntI. Conference on Intelligent Robotsand Systems, Victoria B.C. Canada, October 1998,"Long term activity of the autonomous robot ­Proposal of a bench-mark problem for theautonomy", pp. 1871-1878.

[Nourbakhsh, 1998] Nourbakhsh, I. R., Thefailures of a self-reliant tour robot with noplanner, The Robotics Institute, CarnegieMellon University, Pittsburgh, 1998.

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