An autonomous excavator for landscaping tasksDaniel Schmidt, Karsten BernsRobotics Research Lab, Department of Computer Sciences, University of Kaiserslautern, Germany

Abstract

In the present day the use of big modern construction machines has considerably improved the building process andtherefore increased the output. Autonomous machines could define the next milestone in this area, as such machines canprevent human beings from working in hazardous environments, improve many of the occurring processes and addition-ally lead to higher earnings for construction companies. This paper deals with problems in the field of developing anautonomous bucket excavator, which is equipped with control and perception hardware, and a complex software system.As the safety of the implemented algorithms has to be proved, a simulation environment is used for testing and adjustingthe parameters. A realistic particle simulation of soil and a physics engine deliver this close approximation to reality andmake soil excavation and environment manipulation possible. A novel behavior-based approach for motion control ispresented which allows natural boom trajectories, achieves good environment disturbance compensation behavior, guar-antees safety of the movements concerning human beings and structures, and keeps a high extensibility of the system forfuture improvements.

1 Introduction

Nowadays for a person watching modern construction pro-cesses of urban development or the rebuilding after a natu-ral catastrophe, it is usually hard to believe in which shortperiods of time the desired goals are reached or the statusquo is restored. Besides important economical and scien-tific developments, which improved the construction pro-cesses, another important factor is the use of more efficientbuilding machines. Although the introduction of these con-struction machines widely improved the whole process andlarger and more powerful ones have been developed, anadditional quantum jump in this area cannot be expected,if only rather small technical performance improvementsfollow. Additionally these machines are steered by humanbeings, which are required to be protected from exhaustionor environmental conditions like hazardous environments.A promising approach is the use of autonomous machines,which could work the whole day and therefore lead toshorter construction times and lower building costs. Thearea of building is rather destined for this task as even to-day large excavation pits are precisely planned and providea rather controlled environment.This paper presents the actual status of the AMoBA (Au-tonomer Mobilbagger1) project whose long term goal is tomake a real bucket excavator fully autonomous to performlandscaping tasks on a planned excavation site. This in-cludes equipping the excavator with internal and externalsensors, joint controllers and implementing a software con-

trol system which takes the autonomous decisions based onhuman input and perceived sensor data.As these machines can harm human beings and can dam-age buildings or itself, a simulation environment has to becreated in which sensor data evaluation and control ap-proaches can be safely tested. This requires a dynamicsimulation of the construction equipment and its environ-ment including a physically adequate simulation of soil.As this process is finished a sufficiently powerful controlapproach has to be developed which can safely operatethe excavator in dynamic environments full of uncertain-ties and disturbances.After mentioning related work in the following, a short in-troduction to the excavator system (Sect. 3) follows. Thenthe physics based simulation environment is presented inSect. 4. It allows for evaluation of the novel control ap-proach for excavator control which is based on the iB2C(integrated Behavior-Based Control) architecture [PLB07]and allows an incremental implementation of a robust ex-cavation system (see Sect. 5 and 6).

2 Related workTwo differently motivated approaches exist for modelingthe behavior of particulate material like soil or sand. Thefirst one is used in the area of material research [NHZG07,Sit00, CHPW96] and needs high computational effort tosimulate effects like soil-tool interaction [CHPW96] orrock-flows and mud-flows [GCD+98]. The Discrete Ele-

1Translation: Autonomous Excavator

ment Method (DEM) [MN04, Wit00] used is designed forhigh physical precision, is computationally complex, andcannot fulfill real-time requirements and is often designedfor high performance computing systems [CHPW96].

The second approach, called particle systems [McA00,Hei02], can be found in today’s computer games. Thesesimulate particulate material in real-time by omitting phys-ical correctness [McA00, Gre07] where the overall impres-sion of the material remains “natural” in comparison to re-ality. In these days such simulations often use the parallelcomputation power of modern graphics processing units(GPUs).

Related work concerning the control of autonomous ex-cavators [Can99, BS04, RS97, SBSR99] uses approacheswhich can be described as parametrized scripted joint con-trol. It is based on knowledge about excavation trajectoriesof skillful operators as it is analyzed in [SCK+06]. Usu-ally, a central planner creates the control values from ex-cavation till dumping using a set of parametrized scripts.Such a planner can create smooth movements concerningposition and velocity changes of the bucket. However,in the case of unexpected disturbances, script parametershave to be recalculated, which may take some time and canlead to unwanted freezing of the movement. Furthermore,these scripts cannot be paused at arbitrary times, whichleads to undesired repetitions of scripted steps or a com-plete maneuver.

3 Hardware platform

The real test platform for work at hand is the wheeled18 ton Volvo EW/180B mobile bucket excavator [Vol07],see figure 1. This machine produces lifting forces around100 kN and is almost completely driven by hydraulics.

Therefore, the first step is to equip it with electroniccontrol valves, internal joint and pressure sensors, externalsensors like laser scanners for environment perception andpowerful PC hardware on which the software system runs.

(a)

(b)

Figure 1: Schematic side view of the bucket excavatoras given in [Vol07] (a) and a picture of the real one (b).The depicted dimensions are M = 2.92 m, L = 8.72 m,C = 3.17 m, E = 1.29 m.

The project’s long term goal is to create a completely au-tonomous excavator system which should be able to per-form landscaping tasks on a planned excavation site. Afirst step is to make a specific task like excavating a trenchor modeling a slope autonomous.The input data, a desired three dimensional surface, shouldbe modeled by a landscape architect in an appropriate CADtool.The software system on the bucket excavator needs togather information about the internal excavator state andits environment and should be able control the machine.Therefore, it is currently modified and equipped with jointencoders, hydraulic cylinder elongation sensors, pressuresensors, load cells, electronic valves, DSPs2, EmbeddedPCs, and high level sensors. Until this process is finished,an appropriate test environment is used for evaluating al-ready implemented functionality.

4 Simulated Test Environment

As the excavator is a highly dynamic system, a real-timephysics engine3 is used which allows to simulate the ex-cavator’s masses, and joints. It produces a quite realisticbehavior of the whole excavator and allows for interac-tion with other dynamic elements like walls or other ma-chines. As the excavator and environment shapes are in-cluded, simulated perception sensors like laser scanners,stereo vision cameras, or time-of-flight cameras and theirevaluation algorithms can be tested under realistic circum-stances. Figure 2 shows a screen-shot from the simulation.

2Digital Signal Processor’s3The NEWTON GAME DYNAMICS physics engine is an integrated solution for real time simulation of physics environments. Additional information

can be found under http://newtondynamics.com.

Figure 2: Screen-Shot from the simulation environmentshowing the excavator at the excavation site. The yellowsticks are defined landmarks which are used by perceptionalgorithms for localization of the excavator.

Due the fact that particulate material cannot be realizedwithin this physics engine, an external soil simulation isconstructed and connected to make excavation and land-scaping possible.In contrast to describing soil with a continuum approach,the simulation is based on single particles and their physi-cal constraints. As the granularity of the material is implic-itly considered, the explicit modeling of soil-bucket inter-action or composition is not required. Continuum modelswould need many complex physical rules to correctly han-dle these cases.A single soil particle can be described as a point in thethree dimensional space ~p with a velocity vector ~v drivingit from one position to another over the course of time. Atthe beginning of each time step ∆t, the particle is movedaccording to equation 1. During these fixed calculationsteps, the movement of each particle is taken to be uniformdue to Newton’s first law of motion.

~pnew = ~pold + ~v ·∆t (1)

As all other abilities are specifically added depending onthe physical requirements of the simulation, a force vector~f is introduced. This allows for summing up all forces act-ing on the particle and the determination of the movementvia the second Newtonian law (see equation 2).

~f = m · ~v ·∆t (2)

A better approximation of the simulation to reality can beachieved by adding more complex physical contact laws.If fast computation is required, physical constraints can beremoved or simplified at the expense of loosing precision.The forces acting on each particle during each time stepcan be divided into two types. Uniform ones externally acton each particle independently from its position while non-uniform ones act between particles and their surroundingobjects (other particles or walls).The first types implemented here are gravity ~g, velocity ~v,and global damping or friction d. Their relating forces ~fg

and ~fd are calculated by ~fg = m · ~g and ~fd = ~v2 · d.

The second ones are more complex as they model the in-teraction between each particle and other objects (particles,planes, etc.). Therefore, additional particle properties likethe radius r, which defines the particles influence sphere,are defined. The particles are modeled as smooth whichpreserve their distance to other objects via spring-damperstems. To simulate glued material, an attraction force isproduced in the opposite direction.

• Radius r: The ideal particle is modeled as a spherewith a radius which other particles or walls shouldnot enter.

• Collide spring cspring: a constant which defines theforce by which each particle pushes away each otherparticle

• Collide damping cdamp: damps the collision of theparticle with an object

• Collide shear cshear: defines the shear force that oc-curs if two particles collide

• Collide attraction cattr: produces an attraction forcebetween two particles next to each other

• Boundary damping bd: damped collision of a parti-cle with a wall

~pa

~pb

~va

~vb

ra

rb

~fspring

~fshear

~fattr

Figure 3: Graphical representation of all forces acting ona particle a at position pa with radius ra from particle b(position pb, radius rb). The forces are the longitudinalspring force fspring, the transversal shear force fshear, andthe attracting force fattr.

All of these constants produce the particle collisionbehavior and have to be adjusted very thoroughly. Agraphical representation of all forces acting on the

particles is shown in figure 3. During each calculation

step all colliding neighbors are found for each particle andthe interaction forces are calculated. A 3D cube

presorting structure (see figure 4), in which each particleis inserted depending on its position, immensely reducesthe search effort, as only neighboring cubes have to be

evaluated for possible interaction instead of all particles.

z

y

x

(a)

r

2 · r

r

(b)

Figure 4: Visualization of the 3D presorting structure,used inside the particle soil simulation. As it divides thespace into cubes with side-length r (a), the search for pos-sible colliding particles includes neighboring cells only(b).

Figure 5: Screen-Shot from the particle soil simulationshowing the bucket, which is constructed from particles it-self, and moves through the surface.

The soil simulation proved to produce adequate results andallows to move soil with the excavator inside the simula-tion environment. A screen-shot showing the particle soilsimulation is shown in figure 5.As it is currently implemented to run on the graphics pro-cessing unit (GPU) via the OpenCL4 standard, problemsfulfilling real-time requirements when running on the CPUcould be solved. Although the uniform shape of the parti-cles leads to an unrealistic settlement and only little dump-ing behavior is achieved, the main goal to create a de-formable surface could be reached.The next improvement will be the usage of computationalresources of the GPU to implement more complex inter-action rules based on non-uniform shaped particles. Agood trade-off between computational simplicity and non-uniformity is the construction of a particle from threespheres whose center points are arranged in a equilateraltriangle.

5 Behavior-Based Control Approachfor Landscaping Tasks

Due to the multitude of possible disturbances in the areaof outdoor robotics, an appropriate solution for control-ling the excavation process is required. As behavior-basedapproaches have been shown their suitability for severalapplications [Ark98], the same can be expected for thefield of construction equipment. Here, the iB2C architec-ture [PLB07, PLB10] is used, where behaviors are imple-mented as modules providing a standardized interface forbuilding up large-scale behavior networks (see figure 6).

es a

r

u

F e ,s ,i i

Figure 6: A behavior module with the outputs activity aand target rating r giving information about the internalstate. The inputs stimulation s and inhibition i, are used toset the activation of a behavior which in turn is an upperboundary for the activity.

The coordination of competing behaviors is accomplishedby so-called fusion behaviors having the same interface asbasic behaviors. A further hierarchical level can be intro-duced by arranging several behaviors in a behavioral groupwhich again has the standard behavior interface.

4OpenCL is the open standard for parallel programming of heterogeneous systems. It delivers a standardized interface for using parallel computationpower of the CPU (central processing unit) and the GPU.

The development process for iB2C starts with the top-down design stage where the given task is decomposed intosub-tasks which in combination lead to the desired goals.The task in the work at hand was to create a central struc-ture which is able to control the autonomous bucket exca-vator at a fixed position inside the simulated system en-vironment. As described in [NWHR06] this represents astandard working scenario for an excavator on an excava-tion site.From a control perspective, it should take the current sen-sor information about joint angles and TCP (Tool CenterPoint) and provide the desired TCP pose of the bucket.Laser scanners and perception algorithms deliver the ac-tual surface data and additional information about the de-sired excavation position. Therefore the three dimensionalpoint-cloud is sorted into 2D search grids already contain-ing information about the desired surface. As depictedin figure 7, possible excavation positions are such ones,where a huge amount of soil has to be removed and sur-rounding cells also contain a high difference between de-sired and actual surface height.

z

y

x

Actual surfaceActual surface

Desired surfaceDesired surfaceExcavation areasExcavation areas

Chosen areaChosen area

Figure 7: Visualization of the 2D search grid containinginformation about the actual surface (red), the desired sur-face (green), possible excavation positions (blue) and thechosen excavation position (yellow).

Driving is not included yet, but may be extended rathereasily as only the relating behavior modules have to be de-veloped and installed.The task can be decomposed into the following parts:

1. Make a surface scan with the (simulated) laser scan-ners and identify a target excavation position.

2. Approach the target position with the bucket.

3. Scratch the surface deep enough to dig out the de-sired amount of soil.

4. Move the bucket to a target dumping position.

5. Dump the soil onto a pile at a given position.

Each of them can be decomposed according to the degreesof freedom they have to influence. These are:

• Turn the torso angle (e. g. initial surface scan)

• Adjust the boom length (e. g. approaching the exca-vation point)

• Adjust the TCP height (e. g. digging deep)

• Change the bucket pitch angle (e. g. dumping of soil)

Besides the Conversion Layer containing data conversionmodules, the implementation in iB2C proceeds bottom-up by adding a fusion behavior for each motion direc-tion (see figure 8, Behavior Fusion Layer). On top,each of the DOFs (Degrees Of Freedom) is split upinto a positive and a negative direction represented bythe mbbBasicDriveBehaviour modules (marked asBDB). Furthermore, a fusion behavior for each directioncoordinates the concurrent access of more than one behav-ior. Within this structure, each higher behavior can influ-ence its desired direction command, e. g. increasing or de-creasing the bucket angle, by submitting commands to therespective fusion behavior.The next stage deals with behaviors implementing the re-quired sub-tasks for the excavation process. The Exca-vation Process Behavior Layer contains three behavioralgroups which are used during the five excavation states.Each of them consists of modules which influence one ormore of the motion directions provided by the BehaviorFusion Layer.The InitialScanning group uses the simulated laserscanners to scan the actual surface and store the gath-ered 3D point-cloud. The group ApproachTarget isused three times during the excavation procedure and con-tains behaviors for reaching a specific target TCP pose.It is used to approach the excavation area (ApproachExcavation Position), to move to the dump posi-tion (Approach Dumping Position), and to dumpthe soil (Dump). Removing soil from the surface is per-formed by the Excavation group, see the detailed viewin figure 8. Similar to the strategy of a human driver,the bucket vertically (i. e. the global pitch angle is around−90◦) penetrates the surface until it reaches a depth ofaround 20 cm. Then, a scraping behavior is achieved bya combination of adjusting the boom elongation and thebucket angle while the bucket height and the torso angleare kept, see figure 10. The complete process is performedby four behaviors influencing the required DOFs of thebucket.Each of these basic abilities can be safely evaluated withinthe simulation environment before the development pro-ceeds. This way, the developer benefits from an iterativeimplementation with incremental steps.In order to execute these abilities in the correct or-der, a switching mechanism for the excavation statesis contained in the Behavior Stimulation Layer. TheMasterControl behavior implements a state machineas depicted in figure 9. The transition between states isdone according to the behavior signals (activity and targetrating) of the stimulated behavior groups of the next layer.

CentralControl (Bottom Interface)

BDB torso right

(F) torso angle

BDB torso leftBDB pitch right

(F) pitch angle

BDB pitch left BDB closer

(F) length

BDB further BDB down

(F) height

BDB up

DeltaToAbsoluteValues

(F) torso right (F) torso left(F) pitch right (F) pitch left (F) further (F) down (F) up(F) closer

(G) Excavation

CylinderCartesianConverter

(G) ApproachTarget(G) InitialScanning

MasterControl.

CentralControl (Top Interface)

Switch On

(G) Excavation (Bottom Interface)

(F) ExcavationAdjustTorsoAngle

TurnBucket

PullBucket

AdjustDepth

(G) Excavation (Top Interface) Legend:

behavior

fusion behavior

behavioral group

MCA module

stimulation

activity transfer

target rating transfer

data transfer

Behaviour Stimulation Layer

Excavation Process Behaviour Layer

BehaviourFusion Layer

Conversion Layer

Figure 8: Behavior network of the central control component as well as the contents of one of the contained groups ((G)Excavation, top left).

Furthermore, additional error and pause states are also in-cluded.

Although, in the actual implementation status, only a sin-gle excavation operation type is performed which shapesthe surface step-by-step by scraping the surface, the struc-ture is widely extensible, as already shown for the au-tonomous outdoor robot RAVON [ABF+09]. Parts of thebehavior network concerning driving are sure to be reusedfrom this project in the future.

Simulation tests in the next section prove that the behavior-based approach is able to create the desired trajectories andis stable in relation to occurring disturbances.

Figure 9: State machine of the central MasterControlmodule.

(a) (b)

Figure 10: Idealized desired trajectory of the bucket during the excavation process (a) and time lapse screen capturefrom the simulation (b). First the AdjustDepth behavior is active until the desired value of 20 cm is reached. ThenPullBucket and TurnBucket become active at the same time to constantly decrease the boom length and adjust thebucket angle.

6 Excavation Process EvaluationTo analyze the error compensation ability of the behavior-based control system during the excavation process (seefigure 10), the TCP position is recorded and visualizedin an undisturbed (figure 11(a)) and a disturbed case (fig-ure 11(b)). The orientation of the bucket remained almostconstant. This can be explained by the higher weightingof orientation in relation to position, which is motivated bythe aim to not spill soil during transportation tasks.

X

Y

Z

(a)

X

Y

Z

(b)

Figure 11: Undisturbed (a) and disturbed (b) trajectory ofthe excavation process

In the undisturbed case, the trajectory is smooth and showsthe desired behavior. The other trajectory, in which a dis-turbance occurs while the bucket is pulled back, contains

a loop at the relating position. A force acting for a shortperiod on the bucket in Y-Z direction which may be pro-duced by an obstacle inside the earth, pushes the bucketin the relating direction. As the object is passed, the forcevanishes and the bucket returns directly to the desired tra-jectory and correctly completes the process. This exampleshows the inherent ability of the behavior-based systemto intelligently handle disturbances directly without can-celing the actual process or recalculation of the trajectory.When the bucket excavator will be fully equipped the sametests will be repeated under real outdoor conditions.

7 Conclusion

The created physics driven simulation test environment isa powerful tool for safely testing new perception and con-trol algorithms. Although the soil particle simulation pro-duces acceptable results, related to the goal of modelinga deformable surface within the simulation, the behaviorshould be made more realistic in comparison to reality.Making the particle shape non-uniform will be a possiblesolution.Also the behavior-based control approach proved to createnatural trajectories even in the case of disturbances. Futurework includes the creation of a safety layer which preventsthe excavator from undesired and maybe destructive move-ments and the application on the real excavator. Therefore,more complex sensor information about the environmenthas to be gathered. Here can be thought of additional laserscanners, radar, and time-of-flight cameras.As the long term goal is to produce a fully autonomousrobot operating on an excavation site the aspect of planningwill play an important role. Based on real machine opera-tor knowledge, strategies for efficient landscaping have tobe developed. This includes the comprehension of drivingduring the excavation process.

8 AcknowledgmentThe research work presented in this paper is sponsored bythe Stiftung Rheinland-Pfalz für Innovation. Thanks to thecompany Volvo Construction Equipment (Konz) for pro-viding the excavator and technical information.

References[ABF+09] C. Armbrust, T. Braun, T. Föhst, M. Proet-

zsch, A. Renner, H. Schaefer, and K. Berns.Ravon — the robust autonomous vehicle foroff-road navigation. In Proceedings of theIARP International Workshop on Roboticsfor Risky Interventions and EnvironmentalSurveillance 2009 (RISE 2009), Brussels,Belgium, January 12–14 2009. IARP.

[Ark98] R. Arkin. Behaviour-Based Robotics. MITPress, 1998.

[BS04] D. A. Bradley and D. W. Seward. The de-velopment, control and operation of an au-tonomous robotic excavator. Journal of In-telligent and Robotic Systems, 21(1):73–97,November 2 2004.

[Can99] H. Cannon. Extended earthmoving withan autonomous excavator. Master’s thesis,Carnegie Mellon Robotics Instiute, 1999.

[CHPW96] A. R. Carillo, D. A. Horner, J. F. Peters, andJ. E. West. Design of a large scale discrete el-ement soil model for high performance com-puting systems. In ACM/IEEE Conference onSupercomputing, 1996.

[GCD+98] J.D. Gascuel, M.P. Cani, M. Desbrun,E. Leroy, and C. Mirgon. Simulating land-slides for natural disaster prevention. In 9thEurographics Workshop on Computer Anima-tion and Simulation (EGCAS98), 1998.

[Gre07] S. Green. Cuda particles. Technical report,NVIDIA Corporation, September 19 2007.

[Hei02] J. Hein. Partikelsysteme. Master’s thesis,Universitaet Ulm, 2002.

[McA00] D.K. McAllister. The design of an api for par-ticle systems. UNC Computer Science TechReport, 2000.

[MN04] A. Munjiza and I. NetLibrary. The CombinedFinite-discrete Element Method. Wiley, 2004.

[NHZG07] E. G. Nezami, Y. M. A. Hashash, D. Zhao,and J. Ghaboussi. Simulation of front endloader bucket-soil interaction using discreteelement method. International Journal fornumerical and analytical methods in Geome-chanics, 31(9):1147–1162, January 3 2007.

[NWHR06] D. Neumann, U. Weinbrenner, U. Hester-mann, and L. Rongen. Baugrund und Er-darbeiten, volume Baukonstruktionslehre 1,chapter 3. Frick/Knöll, 34th edition, 2006.

[PLB07] M. Proetzsch, T. Luksch, and K. Berns. Thebehaviour-based control architecture iB2Cfor complex robotic systems. In Proceed-ings of the 30th Annual German Conferenceon Artificial Intelligence (KI), pages 494–497, Osnabrück, Germany, September 10-132007.

[PLB10] M. Proetzsch, T. Luksch, and K. Berns. De-velopment of complex robotic systems us-ing the behavior-based control architectureiB2C. Robotics and Autonomous Systems,58(1), 2010.

[RS97] P. Rowe and A. Stentz. Parameterized scriptsfor motion planning. In IEEE/RSJ Interna-tional Conference on Intelligent Robots andSystems, IROS 97, volume 2, 1997.

[SBSR99] A. Stentz, J. Bares, S. Singh, and P. Rowe.A robotic excavator for autonomous truckloading. Autonomous Robots, 7(2):175–186,September 1999.

[SCK+06] Y. Sakaida, D. Chugo, K. Kawabata,H. Kaetsu, and H. Asama. The analysisof excavator operation by skillful operator.In Proc. of 23rd International Symposiumon Automation and Robotics in Construction,pages 543–547, 2006.

[Sit00] T. G. Sitharam. Numerical simulation ofparticulate materials using discrete elementmodelling. Current Science, 78(7), April 102000.

[Vol07] Konz Volvo. Volvo wheeled excavatorew180c, March 3 2007.

[Wit00] A. Witkin. An introduction to physi-cally based modeling: Constrained dynamics.Technical report, Technical report, CarnegieMellon University, 1997, 2000.