RAVON – An autonomous Vehicle

for Risky Intervention and Surveillance

H. Schafer∗ and K. Berns

Robotics Research LabUniversity of Kaiserslautern (Germany)

May 31, 2006

Abstract

This paper introduces the autonomous outdoor robot RAVON1 (see Figure 1)which is developed at the University of Kaiserslautern. The vehicle is used as atestbed to investigate behaviour-based strategies on motion adaptation, localisationand navigation in rough outdoor terrain. At the current stage the vehicle featurescollision-free autonomous navigation along way points given in GPS coordinates.

1 Introduction

Today the world is facing an increasing frequency of natural disasters, large-scale accidentsand terrorism. Fire infernos, hurricanes, air crashes and attacks on chemical or nuclearplants are only a few possible scenarios. Unmanned vehicles could patrol frontiers, guardindustrial estates, take routine measurements in predefined areas, fulfil reconnaissancetasks in hostile environments or assist in clearance duties in cases of accidents, naturaldisasters or after military conflicts – to name just a few scenarios.

Figure 1: RAVON and the team – from left to right: J. Koch, K. Berns, T. Braun,H. Schafer, N. Schmitz, M. Proetzsch

∗b schaef@informatik.uni-kl.de1RAVON → Robust Autonomous Vehicle for Off-road Navigation

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To meet future demands in risky intervention and environmental surveillance applica-tions the Robotics Research Lab at the University of Kaiserslautern has formulated thedevelopment of an entirely autonomous vehicle for rough and vegetated terrain as longterm goal. The reproducible navigation along a predefined track as well as the autonomousexploration and mapping of unknown environments have been defined as crucial targetcapabilities.

1.1 Motivation

Following recent development in unmanned space travel [Sherwood 01, Schenker 03], agri-cultural automation [Thuilot 01, Lenain 03, Wellington 04], archaeological exploration[Gantenbrink 99] and evolution of military devices [Hong 02, Zhang 01, Debenest 03] itbecomes apparent that the demand for unmanned ground vehicles (UGV) is strongly in-creasing. Still most of the implementations appear more like enhanced remote-controlledcars [Kunii 01] than autonomously acting and decision-taking, thus ”intelligent”, robots.

As most of the applications require a robot to work in relatively unstructured andunknown environments, navigational tasks like ”Move from location A to location B”become complex proceedings. Therefore, it has been regarded as safer for the investment,in terms of the robot hardware, to let humans take most of the decisions necessary for arobot to accomplish its mission.

One crucial drawback of this approach is the fact that high bandwidth communicationwith the control headquarter consumes most of the energy the robot carries around. Thismay be quite annoying as recharging might be complicated or time-consuming. Imaginea planetary rover on Mars. How would you recharge its batteries once they ran low? Atbest you will have to wait for the solar panels to collect enough energy to resume theoperation interrupted when the batteries failed. The same holds for any system relyingon electric actuators. Although in most cases combustion engines would solve the energyproblem satisfyingly [Fukushima 01], there are many reasons for nevertheless bankingon battery-powered systems. Augmented noise, weight and pollution shall suffice to bementioned here.

Furthermore remotely deciding what actions are safe is not always easy from thesensory data provided by a mobile robot. Most of the time this will be video images, whichare normally 2D, limiting depth perception. In general a lot of training will be necessaryin order to master vehicle control in difficult situations. Introducing more sophisticatedperceptional systems and preprocessing units will probably render remote-controlling evenmore complex.

Recapitulatory current remote control mechanisms result in long operation times andhigh costs for qualified personal. Both issues shall be addressed with self-dependentnavigation systems which enable mobile robots to find their way to a given target areaautonomously.

1.2 Structure

In the following section an overview of the state of the art in autonomous navigationwill be given. After that the fully autonomous robot RAVON shall be introduced inSection 3. The achievements in the current stage of development will be discussed inSection 4 leading to an outlook on future work to be presented in Section 5. At the endof the paper the reader will find a short presentation of the team members as well as the

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bibliography.

2 State of the art in autonomous navigation

Many different aspects of autonomous robot navigation in outdoor terrain have beeninvestigated until now. A lot of publications deal with rather flat and near-obstacle-freeenvironments like roads or parks. In contrast scientific work on navigation in rough andvegetated terrain is a lot harder to find.

2.1 Terrain featuring virtually no vegetation

As already mentioned above a large block of work on autonomous vehicles has been done inthe domain of road following. In this context in particular the NAVLAB vehicles (see Fig-ure 2 (a)) developed at Carnegie Mellon University should be mentioned [Williamson 98].These systems use real-time-capable neural networks in order to generate steering com-mands on the basis of video images. After a training phase the vehicle has been able tofollow certain types of roads in an autonomous manner.

Another huge field of application is the space exploration sector. Though a lot lessstructured than roads on earth, terrains on moon or on mars can be considered free ofvegetation. That way the approaches published by NASA / JPL2 [Olson 00] (Figure 2(b)) or MD Robotics [Se 04] aim at environments that are dominated by jagged rockyformations. Furthermore the degree of autonomy is very limited. [Lacroix 02] introducesa robot for planetary missions which features elaborate facilities for local obstacle detec-tion, localisation and navigation. Yet the computational effort only allows for very slowvelocities (about 10 cm/s).

Figure 2: Vehicles for terrain without vegetation (a) Navlab 11 by Carnegie Mellon Uni-versity, (b) Sojourner by JPL, (c) Stanley by Stanford University (Winner of DARPA GC2005)

The third important branch of work on self-dependent vehicles for unvegetated terraincomprises efforts which are related to the DARPA Grand Challenge (see Figure 2 (c)). Inthis context a number of vehicles suitable for the traversal of desert and steppe have beenintroduced [Urmson 04]. As the competition rules incorporate severe time constraints thefocus is in particular on high-speed navigation (about 30 km/h) . On the other handthe terrain chosen is in general of minor jaggedness and rather free of difficult-to-detectobstacles. Furthermore a priori knowledge of the terrain to be traversed is available tothe competitors in terms of a global map and a route in GPS coordinates.

2JPL → Jet Propulsion Laboratory: http://www.jpl.nasa.gov/

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2.2 Terrain featuring vegetation

At universities in Hannover [Brennecke 04] and Sydney [Bailey 02] trials concerning au-tonomous driving in parks and grassland have been carried out. Such environments excelas locally flat with sparce and evident obstacles like trees which can as well be usedfor landmark-based navigation. The methods developed intensely exploit this a prioriknowledge such that their application in hilly and largely vegetated areas – like forestsfor example – is clearly limited.

An earlier approach banking on behaviour-based control is presented in [Langer 94].Based on stereo vision and laser range information a statistical traversability map is gen-erated. This local map is consulted to chose a feasible trajectory. The central capabilitiesof the vehicle comprise obstacle avoidance, homing and road following. The results showtest runs in open land with a low density of obstacles. The approach in general suffersfrom the binary representation of untraversable terrain as this may block the vehicle inrough terrain.

Figure 3: Vehicles for vegetated terrain (a) Parkboy by Hannover University, (b) Navlab2 by Carnegie Mellon University, (c) DEMO III

Of all activities in the field of outdoor robotics the work carried out at the JPL[Bellutta 00, Talukder 02, Manduchi 05] – in the context of the DEMO III project of theU.S. Army [Shoemaker 98] – is related most to the research at the Robotics ResearchLab of the University of Kaiserslautern. The goal of the work is the development of anautonomous military vehicle in strongly vegetated and cluttered terrain. Discriminatoryfor these projects is the large variety of expensive technology (LADAR, high-resolutionstereo systems and powerful computational units) as well as the semi-automated systemarchitecture. Exploration and global navigation as well as tackling difficult situations aresupported by a human operator.

3 The mobile robot platform RAVON

In the following the research platform RAVON (see Figure 4) which in recent years hasbeen implemented at the Robotics Research Lab of University of Kaiserslautern shall bepresented.

3.1 Mechatronics

RAVON is a four wheeled off-road vehicle based on the RobuCarTT platform by Robosoft.With 2.35m length and 1.4m width the 400 kg robot is relatively large. The vehicle

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Figure 4: RAVON in rough outdoor terrain

features a four wheel drive with independent motors yielding maximal velocities of 3m/s.In combination with its Hankook off-road tires the vehicle can climb slopes of 100%inclination which makes it fit for the challenges in rough terrain. Front and rear axis canbe steered independently which supports agile advanced driving manoeuvres like doubleackerman and parallel steering.

Figure 5: (a) Inertial measurement unit; (b) Spring-mounted safety bumper; (c) HF aerial;(d) Universal data transceiver plus amplifier

The motors are powered by several lead batteries which also take care of alimenting thesensor systems and the industrial PCs which have been customised by DSM Computers.The electronic parts have been encased into a custom design chassis which has beenproduced in the MiniTec profile system and is water and shock proof to a reasonabledegree.

In order to navigate in a selfdependent fashion RAVON has been equipped with severalsensors which enable the robot to perceive its environment. For self localisation purposesthe robot uses its odometry, a custom design inertial measurement unit [Koch 05] (seeFigure 5 (a)), a magnetic field sensor and a DGPS receiver. On its way to the designatedtarget area the robot may come across a variety of obstacles which can be detected usingthe stereo camera system mounted at the front of the vehicle. This obstacle detectionfacility is complemented with two SICK laser range finders (field of vision: 180 degrees,angular resolution: 0.5 degrees, distance resolution: about 0.5 cm) which monitor theenvironment nearby the vehicle.

In case of urgency the system will be stopped on collision by the Maysersafety bumperswhich have been integrated into spring mounted push rods (see Figure 5 (b)) at thevehicle’s front and rear side. These are directly connected to the emergency stop toensure maximal safety. In the future the compression of the spring system shall be usedto detect occluded obstacles in situations where geometric obstacle detection cannot beused.

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In order to access, monitor or even control the vehicle over longer ranges a universaldata transceiver produced by IKHF has been mounted on the robot. In combination witha 500mW amplifier distances up to 10 km can be managed at a data rate of 115 kbit/s.An appropriate omni-directional HF aerial has been mounted onto a gimble to ensurevertical alignment of the antenna at severe slopes. Figure 5 (c) and (d) illustrate theconfiguration.

The technical drawing3 in Figure 6 illustrates the dimensions of the robot as well asthe mount points of the integrated systems.

Figure 6: Technical drawing of RAVON (top view)

3.2 Navigation Software

So far the robot hardware has been described. In this section RAVON’s navigation andcontrol software shall be introduced. Figure 7 illustrates the top-level structure of thesystem.

In order to operate the vehicle the navigation system can be provided with a graphof interconnected way points (e.g. GPS coordinates). When assigned to a target area,the Navigator module searches a feasible path according to the graph of way points. Thecorresponding drive commands are step by step sent to the Homing Behaviours whichtry to reach the given position. The Navigator and the Homing Behaviours are bothsupplied with pose estimations by the Localisation module. On its way to the designatedtarget area the robot may come across a variety of obstacles. The Obstacle Detectionmodule processes visual and tactile sensors in order to determine the traversability ofthe terrain ahead. This information is interpreted by the Hazard Avoidance Behaviourswhich suppress the control signals of the Homing Behaviours in order to initiate an evasivemanoeuvre if hindrances are present. That way these two components compete with eachother introducing course correction whenever necessary.

The navigation system runs under linux which has been installed on the robot’s indus-trial PCs. At some points (e.g. the IMU or the magnetic field sensor) the sensor data ispreprocessed on custom-design DSP boards. All software is embedded into the modularC++ robot control framework MCA24 which is developed at FZI5 in Karlsruhe.

3For further development the basic technical drawings of the RobuCarTT platform have kindly beenprovided by robosoft.

4MCA → Modular Controller Architecture: http://mca2.sourceforge.net5FZI → Research Center for Information Technologies: http://www.fzi.de/

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Figure 7: Structure of the vehicle control system

In the following sections the central parts of the navigation software will be discussedin more detail.

3.2.1 Behaviour-Based Motion Control

Classical control approaches often require a complete physical model of the robot and theenvironment. The target terrain and the double Ackerman kinematics of the robot inprinciple prohibit a complete and sound description of such a model. The steering modeldepicted in Figure 8 illustrates that – even on flat ground – wheel slippage is an immanentpart of the vehicle’s dynamic, as a unique centre of rotation does not necessarily exist.In rugged and vegetated terrain the ground parameters change permanently such thatslippage and skidding cannot be ignored. Therefore, RAVON features a behaviour-basedmotion control system which does not require a complete knowledge of the environmentto achieve robust locomotion.

The behaviour-based architecture according to which the control software has been im-plemented is a modified derivative of the model introduced in [Luksch 02] and [Albiez 03].The modifications made are partly exemplified in [Proetzsch 05]. Performance and flex-ibility of the approach have been demonstrated in a case study which is presented in[Schafer 05c]. Additional concepts like hierarchical behaviour grouping shall be publishedsoon.

The fundamental unit of the proposed control architecture is the behaviour-basedmodule. Each atomic behaviour is wrapped into such a module which computes themeta output signals activity and target rating. The impact of behaviours on the overallcontrol of the robot can be influenced using their meta inputs activation and inhibition.The meta signals allow the arrangement of behaviours in a comprehensive hierarchicalfashion, which supports the extension of the control system without touching existingbehaviours and interconnections. As already alluded above, related behaviours can bewrapped into behaviour-based groups which comply to the same interface as behaviour-

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Figure 8: Steering model of RAVON in double ackerman manoeuvre

based modules. That way another hierarchical level can be introduced to manage complexbehaviour networks.

The behaviour network implemented on RAVON comprises three control chains affect-ing desired rotation, side-slip, and velocity of the vehicle. The rotational and the trans-lational components are influenced by slope observing behaviours and obstacle avoidancefacilities. The underlying obstacle detection mechanisms will be introduced in detail inthe next section. For safety reasons the velocity is adjusted according to obstacle prox-imity and critical vehicle inclination. This control system has proven robust and suitablefor locally flat outdoor terrain [Schafer 05a].

3.2.2 Localisation

One major challenge in the development of mobile robots is self localisation. Outdoorsoften three dimensional information is necessary due to uneven terrain. Some applicationseven require additional information like attitude or velocities. Robustness and adaptabil-ity are additional needs which have to be satisfied by a powerful localisation system. Bestpossible information has to be provided despite sensor loss or sensor malfunction.

For the reasons given above a fault-tolerant system architecture has been chosen whichcan cope with dynamic changes in sensor availability [Schmitz 06]. Based on a linearKalman filter and a flexible model of the system and the sensors the Localisation moduleestimates the system state based on the available sensors. The current sensor equipmentconsists of odometry, an inertial measurement system, a GPS receiver, and a magneticfield sensor (see Figure 7). Additional sensor modules like visual ego motion estimatorsor natural landmark detectors can be easily integrated.

3.2.3 Obstacle Detection

When working in rough terrain, robots necessarily encounter places they cannot pass.Even if a priori knowledge is available – for example an elevation map of the terrain asprovided at the DARPA GC – the robot will need certain mechanisms to decide whatlocations are safe and which places it should avoid. As RAVON at the current state ofdevelopment features purely GPS guided navigation along routes which are stored in a

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topological map, obstacle detection is another crucial aspect for mission success. Thepath between way points may feature hindrances, the GPS coordinates may be noisy orchosen unfavourable. In any case the robot needs to find its way to the target area.

The principal sensor for obstacle detection is the stereo vision system which is mountedat the front side of the vehicle. A detailed description of the detection mechanism canbe found in [Schafer 05d]. Inspired by obstacle tracking in humans, the stereo camera’snarrow field of vision is compensated by local obstacle memories to either side of therobot. These keep track of obstacles which have already left the stereo system’s activefield of vision. The stereo-vision-based obstacle detection is complemented with two laserrange finders which monitor the area nearby the vehicle. If an obstacle enters this zonethe robot will set back and try to find another way. If all mechanisms fail, RAVON isequipped with safety bumpers which switch the vehicle off on collision. The itemisedsafety zones are visualised in Figure 9.

Figure 9: Regions monitored by the obstacle detection and avoidance facilities

At the moment all visual sensors are fixedly mounted onto the robot. For that reasonthe laser scanners are used for close-range detection only as vehicle tilt and roll makeevaluation of faraway range values obtained in a single plane quite difficult. In the futurethe laser scanners shall be equipped with a panning or rotating unit in order to gainmore information. Likewise the stereo system will shortly be mounted onto a pan- andtiltable unit in order to better exploit the field of vision. Furthermore the spring-mountedsafety bumper shall be used to realise a tactile sensor which shall permit traversal of highvegetation at very low speeds. In that context the deflection rate of the push rod willserve as a measure for the rigidity of the geometric entity ahead before the safety bumperis triggered.

4 Experiments and Results

RAVON has mastered several scenarios in outdoor terrain featuring a low density ofobstacles. Figure 10 shows one test run consisting of seven way points which form arectangle of about 25m times 30m. The satellite image is superimposed by the way points,the position estimated by the Localisation module (solid line), and the GPS measurements(dotted line). The estimated position is marked with a large dot every 50 s. The maximumdeviation from the test points has been about 2m during several test runs. A longer

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test with 5 sequenced test runs has shown the long time stability and precision of thelocalisation system.

Figure 10: Testing grounds (l) and satellite image overlayed with the position plots (r)

Obstacle detection and avoidance capabilities are shown in Figure 11. The robot’spoint of view in terms of the left camera images and the reconstructed depth maps havebeen associated with activity traces resulting from a subset of the obstacle avoidancebehaviours. KeepDistLeft monitors the terrain to the left side of the robot and initiatesevasive manoeuvres to the right if necessary. KeepDistRight does the same for the terrainto the robot’s right.

Figure 11: Obstacle avoidance test run

In this scenario the robot has to pass through a very narrow passage way of busheswhich is located nearby the Robotics Research Lab. The traces clearly show how KeepDis-tLeft and KeepDistRight interact in order to guide the vehicle safely through the bushes.

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Whenever the robot comes to close to the right side of the passage (see Figure 11 Check-points (1), (3) and (4)) KeepDistRight becomes more and more active which results intoa steering manoeuvre to the left. Contrarily KeepDistLeft assures that the robot will notcrash into the left bushes following the same pattern (Checkpoints (2) and (5)). Note thatmany more behaviours interact in the actual control system. For simplicity reasons onlya subset has been mentioned. Further behaviours slow the robot down when obstaclesare close and arbitrate if the two behaviours introduced above cannot find a solution inparticular driving situations. For more detailed descriptions of the concepts underlyingthe behaviour network the reader shall be referred to [Schafer 05b, Schafer 05c].

5 Summary

In this paper the autonomous outdoor vehicle RAVON has been presented on a high levelof abstraction. The targeted field of application – rough vegetated outdoor terrains –requires a robust control system which relies on various complementing sensor systems.The behaviour-based control approach which has been presented combines reactive motioncontrol and higher level navigational tasks into a powerful navigation system.

Currently the robot is fit for hilly grassland with a low density of obstacles featuringsparse vegetation. The capability to avoid dangerous situations as well as the perfor-mance of the localisation system has been shown in several test runs. A brief overviewof the results has been given in this work. Repeated integration tests in more difficultterrain are to be carried out shortly in order to prove the system’s qualification for furtherdevelopment.

In the future the localisation system shall be complemented with visual approachesfor the identification and recognition of navigation relevant places. This will serve as abasis for the Navigator module (see Figure 7) to perform exploration tasks in unknownenvironments in a self-dependent fashion. Furthermore this module shall more tightly belinked to the rest of the behaviour-based control system. By observation of the hazardavoidance behaviours the Navigator could get an idea of how feasible a path between twoway points actually is. If a lot of course corrections are necessary it might be useful tointroduce another way point inbetween or perhaps to use another path next time.

The obstacle detection facilities need to be extended to cope with intense vegetation.At the moment the geometric evaluations do not permit the robot’s application in envi-ronments featuring for example high grass. The visual system would classify the grassas non-traversable and the robot might be stuck. In that context texture classificationapproaches will be combined with the tactile sensors which have already been integrated.That way the vehicle shall be enabled to pass through high but flexible vegetation in orderto find its way to the designated target area.

Acknowledgements: During the development of RAVON we have received a lot oftechnical and financial assistance. We would like to thank our sponsors at this point fortheir dedication: IKHF (Ingenieurbetrieb Kunze - HF Technik), Mayser Polymer Electric,Hankook Tyres, Minitec, SICK, DSM Computer, Robosoft

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