AREA COVERAGE STUDIES FOR AN AUTONOMOUSDEMINING ROBOT

Jose Prado, Goncalo Cabrita and Lino Marques

Abstract— This work presents the autonomous areacoverage with a lightweight mobile robot equippedwith a mine detecting sensor head attached to theend-effector of a two degrees of freedom (2 DOF)actuator arm. Two coverage techniques were employedin this work, line-sweeping (LS) and spacial cell dif-fusion (SCD). The problem of improving the motionof the platform and the arm, in terms of: numberof maneuvers, traversed distance, percentage of areacovered and time was addressed. The feasibility andeffectiveness of both algorithms are demonstrated bysimulation results for coverage work of target area.

I. INTRODUCTION

Humanitarian demining is a risky task which usu-ally leads to several victims among the deminers.Therefore, significant efforts are being made by thescientific community, in order to develop systems ableto automatically detect landmines [1], keeping theusers as safe as possible. Blast antipersonnel minesinclude less than 100 g of explosive and very littlemetal. Resorting to simple construction techniquesthese can be very cheap (around one US dollar) andas a consequence are extremely widespread. Due tothe typical diameter of about 10 cm when diggedor surrounded by grass they are very difficult tosee. Blast mines are triggered by a ground pressureof about 10 kg/dm2, these mines are not designedto kill but to badly maim [2]. Although mines areusually located close to the surface and present smallamounts of metal, a mine field usually containscountless harmless metal objects of different sizes ona multitude of depths which contribute to the highfalse alarm rate.

This work is a part of the ongoing projectTIRAMISU (ISR, Coimbra, Portugal) aimed to de-velop demining technologies; among robots, sensorsand devices that endows the exploration of a certainarea and the location of landmines. In order todevelop a system for landmine detection, some keymeasurable features, that characterizes the landmine,shall be properly selected. This issue is not a trivialtask due to the fact that there is a large variety ofantipersonnel landmines which differ from each other

J. Prado, G. Cabrita and L. Marques are with the Departmentof Electrical and Computer Engineering, Institute of Systems andRobotics, University of Coimbra, 3030-290 Coimbra, Portugal.{jaugusto, goncabrita, lino}@isr.uc.pt

Fig. 1. Robot equipped with dual-sensor in the mobile arm.

Fig. 2. Sample of anti-personal landmines with different shapes.

in its used materials, shape and size (see Figure 2taken with permission from [3]). Different approacheshas been proposed for the autonomous mine detection[4], [1]; the used sensors vary among metal-detectors,ground penetration radars, cameras, chemical sensorsand others, or even the fusion of some of thesesensors. However, the fact that the robot can onlysense the mine when it is really close to it is aconstraint present in all the scenarios. If the purposeof the robot is to map and not trigger the mines, thisconstraint affects the possible coverage strategies.

Henceforth, in this paper a comparison betweentwo coverage methods is made, taking into accountthe defined assessments and the current robotic plat-form 1, in order to reduce the cost/energy and timeof the coverage task for the entire designated area.

II. RELATED WORK

Covering large areas is a task that is obviouslyuseful in a large number of applications such ascleaning large surfaces, mapping purposes [5], andautonomous mine clearance [6].

While in indoor environments, the obstacles are

Fig. 3. Multi-sensor detection process after a combined robotand arm sweeping.

usually walls, furniture and structure of the building,in outdoor environments there can be trees, rocks,ravines; in either case an obstacle free environmentis very rare. More specifically, for the demining task,the mines by itself shall be considered a special typeof obstacle, that after detection, shall be mappedand avoided. There are several approaches for cov-erage in the literature, a line-sweep-based approachis described in [7], a spatial cell diffusion approachis presented in [8], in [9] a multi-robot coveragealgorithm is presented, a smooth spiral path planningis studied in [10], an olfactory coordinated area cov-erage approach is described in [11]. When combiningthe movement of the platform with the movement ofthe arm, different approaches will certainly lead todifferent energy consumptions. Thus it is necessary toanalyze which approach requires less energy, withoutlosing the certainty of performing a complete areacoverage.

III. ROBOTIC ARM SWEEPING

In order to detect and localize anti-personnel land-mines with our dual-sensor equipped robot, a com-mon approach is to cover all the area of a gridstructured area. Due to the configuration of our robot,it was defined several cells with dimensions 1m x1m each, those can be easily covered by the armsweep, in front of the robot. Since each coil ofthe metal detector is equipped with one chemicalsensor, the swiping movement of the arm generatessimultaneously 3 lines of dual data. These dual sensordata is fused and interpolated based on the methodproposed at [12].

Although an arm movement of 180 degrees wouldmaximise the coverage area in front of the robot,the arm maximum angle also affects the maximumvelocity of the robot. Thus we set the maximum angleof arm sweeping as 90 degrees, 45◦ to the left and45◦ to the right. The sweeping speed recommended

by the manufacture of our metal detector is 0.25m/s. Since our robotic arm has 1 m of extension,the distance of one arc sweep is π

2 m. The coveredstrip, while the robot moves forward, has a detectionwidth of

√2 m. Therefore we setup the arm speed

at 0.25 m/s and the robot forward (scan speed) at1

30 m/s. The rotation speed of the robot when stoppedfor maneuvers was defined as the maximum rotationspeed, since no scan is desired during this type ofmaneuver. The fusion with interpolation enhances thegenerated map, providing thus a dense map of theregion in front of the robot, instead of sparse lines.Samples of the output of the interpolated fused dataare shown in the right column of figure 3. This speedconfiguration guarantees the covered movement rep-resented in the top left corner of figure 3, endowinga dense map of the arc region in front of the robot.The probability of mine presence varies between 0and 1 (i.e. 0% to 100%), therefore high peaks ofprobability are added to the navigation costmap [13]as being obstacles. Therefore, navigation continuesthe coverage algorithm while mapping and avoidingthe discovered mines.

IV. COVERAGE

First of all, let’s make some assumptions aboutimportant elements that describe a coverage problem:• The coverage region: The region to be covered

is continuous and smooth (or can be embeddedin the plane), is connected, and is defined byan outer perimeter and some mines (obstacles)in its interior. In this paper we will assume thatperimeter is polygonal, and the mines are spreadrandomly.

• The robot: It has nonholonomic constraints, itis a differential skid steer robot, and the shapeof the robot is unrelated to the sensor/actuatorpattern. The starting or ending position of therobot may be specified.

• The sensors: The sensor or actuator has a one ortwo dimensional coverage pattern which sweepsout a two dimensional area as the robot moves.However the sensor moves, it is assumed that thegenerated sensor/actuator pattern does not moverelative to the robot. The sensor pattern is largerthan the robot. This is necessary because duringthe coverage of a mined field it is necessary thatthe robot moves towards a “clean” subregion,and it must fit in this subregion.

• The robot can navigate outside the target areain order to maneuver, thus the map is always atleast one cell larger in every direction than thetarget area.

• A coverage algorithm must return: The coveragepath through a detailed sequence of motion

(a) Line-sweep trajectory (b) SCD trajectory

Fig. 4. Target trajectories for both algorithms.

commands for the robot.

A. Line-Sweeping

The line-sweeping algorithm works in two stages.In the first stage, the algorithm selects the longestedge of the field to determine the optimal directionof the sweep. In the second stage, it generates n tracksparallel to the field edges from the inside to be usedas rows.

For a given rectangular area of size m×n, wherem≥ n and w is the width of the sensor detection area.The number of lines is defined as n/w. The coveragealgorithm than sends to the navigation cartesian co-ordinates of the points of turn, defining the optimaltrajectory as represented in figure 4(a). Since we areconsidering that the testing area is strongly connected,a mine field with sparse and randomly distributedmines, this trajectory is achieved without the needof further area segmentation.

B. Spatial Cell Diffusion

The SCD algorithm moves the robot in a spiralmovement, alternating clock-wise and counter-clock-wise movements each time robot get stuck or reachesto a border of the target area. This algorithm per-formed well for areas with dense obstacles and lowconnectivity, as rooms and indoor environments [8].

According to the direction of search selected, robotnavigates and sweeps the target region in consistentmanner. While executing a coverage operation, therobot changes its direction of motion and reverses thesense of rotation at a boundary region of work spacemodel. Alike in the line-sweep, the coverage SCDalgorithm than sends the coordinates to the navigationplanner, as represented in figure 4(b).

V. METHODOLOGY

A. Energy

Since batteries are a limited resource, and energysources are often not abundant near mined field areas,an important factor in field robotic coverage is tooptimize the energy consumption of the robot. Due

to the configuration of our robot, let’s model C(T ),the energy cost function for the covered area as:

C(T )=α

∫ T

0|ω(t)|dt+β

∫ T

0|v(t)|dt+γ

∫ T

0|s(t)|dt

(1)where:• ω(t) is the velocity of rotation performed by

the robot’s body,

• v(t) is the linear velocity of the robot,

• s(t) is the arm rotation speed,

• α is the rotation consumption, in W.h/◦,

• β is the linear consumption, in W.h/m.

• γ is the consumption of the arm sweeping inW.h/◦.

Energy consumption of the robot was measuredin different situations inserting a current and voltagemonitor between the robot’s battery and its electricloads. Thus, the constants of the cost function are:α = 154,68W.h/◦ (average for robot rotation), β =25,78W.h/m (average of forward movement at scanspeed), and γ = 3,86W.h/◦. Since these are consump-tion constants, different coverage algorithms varies interms of number of maneuvers, traversed distance andtotal time, which directly influences the parameters∫|vel|dt and

∫|ω|dt. Therefore, these parameters

were measured, in a simulated environment.

B. Simulated Environment

A virtual 2D world created in Stage simulatorwas used as the simulated environment. The robotfootprint has the dimensions of the Husky robotpresented in figure 1, the 2D top view size of therobot is 0.81 m X 0.67 m. The arm dimensions werealso designed accordingly, 1 m arm length and thearm sweeping angle was limited to 90 degrees. Thenavigation commands issued to this simulated robotare the same velocity commands issued for the realrobot. Since in the real robot we plan to use GPSRTK and a very accurate localization is expected, inthe simulator perfect odometry is been used as thelocalization reference. The sensor fusion techniquepresented on [12] results on a probability map ofmine presence along the scanned area. Thus, in orderto simulate the sensors, high peaks of probabilityof mine presence were injected on the scanned areawhen mines are found in that area of the map. Thisconfiguration combined with the coverage techniquesallows representation of the detection map whencoverage is complete.

(a) Line Sweep (b) SCD (c) Mine Probability Map

Fig. 5. Results of simulation in stage.

Henceforth, 3 mines were randomly positioned ina 10 m× 10 m maneuver space. Moreover, since therobot needs to be able to maneuver outside the targetarea, a 9m×9m inscribed sub-area was defined as thetarget area to be covered. The position of the minescan be seen (as small black points) together with therobot and sensor trail, the target area is marked by adashed line, see figures 5(b) and 5(a).

C. Assessments

As the objective is to improve the motion of theplatform together with its jointed arm, several assess-ments variables were defined for the metrics. Thetwo analyzed coverage algorithms were compared interms of:

• Energy: Energy consumption of the robot for theentire covered area.

• Number of maneuvers: the forward linear motionis more efficient in terms of energy consumption,thus the maneuvers shall be reduced.

• Traversed distance: the total time of coverage isdesired to be low, however since the total time isrelative to the speed of the robot, we normalizedthe time by the speed and decided to use the totaltraversed distance as a metric.

• Percentage of overlapping: the percentage ofarea that is covered more than one time. In aideal case, the robot establishes a path that doesnot need to repeat any cell or any subarea ofthe target area. However, in most cases this isnot possible due to the obstacles and speciallybecause in the demining task the robots does notknow a priori where are the mines.

• Percentage of area covered: the percentage ofarea covered is required to be 100%.

• Mine detection rate: the quality of the generatedmap will be better analyzed when field testsare realized, however for now we are using thismetric to know if the robot detected all the minesor if it missed some of them.

VI. EXPERIMENTS

In order to evaluate the robot’s behavior duringeach of the analyzed coverage algorithms, the Stagesimulator was used. Stage provides a virtual worldpopulated by mobile robots and sensors, along withvarious objects for the robots to sense and possiblymanipulate. Each algorithm was executed 10 times inthe simulation space, with mines distributed randomlychanging position and amount of mines. The amountof mines varied between 1 and 5 mines and the spaceand shape of the target area was kept constant as9m×9m inscribed in a 10m×10m maneuver space.The defined assessments were measured and anno-tated, with special attention to the energy consump-tion. The size of the simulated landmines was alsokept constant for all tests. The simulated landminessize was based on the mean size of the probabilitypeak generated by the detection method proposedin [12] when scanning the M114 (also known asMAPS), an old Portuguese made anti-personal blastmine. Samples of the robot and sensor trail for bothalgorithms, in one map configuration, can be seen infigure 5.

VII. RESULTS AND DISCUSSION

The results presented in table I are the mean ofthe 10 times performed simulation per each of thealgorithms. Notice that in the number of maneuversthe amount right of the plus sign indicates smallmaneuvers that the robot performed in order to avoidstepping on the mines, while the number at left ofthe plus sign are about the maneuvers to keep thecoverage path. The mine detection rate was 100% forboth cases since none of the current tests presenteda false-negative (a miss-detection). An example ofone probability map of mine presence is presented infigure 5(c). The traversed distance was very similar atboth algorithms, in the current tests this is acceptablebecause of the sparsely distributed mines. In futurework we plan to investigate the behavior of these

algorithms with a dense set of mines among subareasof the field.

TABLE IPERFORMANCE COMPARISON BETWEEN THE TESTED

COVERAGE ALGORITHMS.

Line-Sweep SCDEnergy C(x) 22W.h 23W.h

Number of maneuvers 17+3 25+3Mine detection rate 100% 100%Traversed distance 80,1m 82,3m

Percentage of overlapping 30% 27%Percentage of covered area 100% 100%

VIII. CONCLUSIONS

This paper addresses the problem of optimising theenergy consumption for a coverage task done with ademining robot equipped with a 2DOF scanning arm.In such setup, the end-effector motion results from themotion of the platform or the arm, or both. A costfunction for these possible end-effector movementswas defined and used to evaluate the global costof covering a given area with different coveragealgorithms. This paper describes ongoing works inthis area. In the final version, a deeper study of theglobal cost influence of different coverage algorithmsfor different types of environments will be provided.

ACKNOWLEDGEMENTS

This work was partially carried-out in the framework ofTIRAMISU project (www.fp7-tiramisu.eu). This project isfunded by the European Community’s Seventh FrameworkProgram (FP7) under grant 284747.

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