Exploration of Underwater Structures with Cooperative Heterogeneous Robots Dirk Spenneberg
Christoph Waldmann
Richard Babb
Robotics Group MARUM Underwater Systems Laboratory Dept. of Math. and Computer Science Center of Marine Environmental Sciences Southampton Oceanography Centre University of Bremen University of Bremen Southampton SO14 3ZH Bibliotheksstr. 1, 28359 Bremen, Germany Leobener Str., 28359 Bremen, Germany UK
[email protected] [email protected] [email protected]
Abstract— This paper describes ideas in the field of cooperative underwater robotics, which can considerably enhance the exploration capabilities of underwater robots. Three heterogeneous planned or existing underwater robots are presented and possible approaches for cooperative behavior are discussed. Possible application scenarios for future deployment are presented.
I. I NTRODUCTION This paper describes ideas in the field of cooperative underwater robotics, which would considerably enhance the exploration capabilities of underwater robots. The authors believe that it will be fruitful to form a bridge between the work on cognitive, cooperative, and adaptive robotics in the artificial intelligence and autonomous robots community and the work in marine underwater robotics, with the goal to develop concepts to develop, introduce, and test state of the art robotic approaches in an underwater environment. The use of multiple underwater agents for defined tasks is currently an important topic within civil and military robotic research although all attempts are still in their infancy. Several projects (like [6]) are underway that make use of homogeneous platforms to evaluate cooperative behavior. In this case cooperative behavior is realized as swarming behavior. Other cooperative concepts exist for individual AUV systems where the control algorithm are based on behavior methods [7]. Cooperative behavior in underwater robots will enable new observational strategies in the fields of geo-sciences, environmental monitoring, and marine biology. As a first result of using cooperative heterogeneous systems in underwater missions, we expect a significant enhancement in the quality of underwater exploration in unknown and difficult sea-floor terrain. Underwater exploration scenarios are an ideal proving ground for the idea of cooperative heterogeneous robots, because the underwater environment is optimal for making use of agents which differ largely in their morphology, their mobility, and their sensors. Deployable robots can have a very different way to act and to perceive their environment and thus they will need / have different ways to represent their surroundings. From the embodied cognition point of view, it is highly interesting to develop mechanisms to exchange, to translate, and to integrate these different spatial representations. In this paper, we present first ideas of how we can use already existing
Fig. 1.
The Autosub
and planned underwater robots to cooperate in an underwater exploration scenario. II. T HE H ETEROGENEOUS ROBOTS In difficult terrains like the bottom of a chasm or inside a cave, communication means are very limited. In comparison to a single system, a group of cooperating robots has higher chances to deal with these limitations; thereby the operational range for a scientific mission can be largely extended. Groups of heterogeneous robots open up new ways to make use of communication networks. For example, a free swimming robot can serve as a relay station for bottom-bound robots. The Southampton Oceanographic Centre has developed over the last ten years the free swimming AUV Autosub (see fig. 1), intended for both scientific and commercial applications [1]. The vehicle is a streamlined torpedo shape seven meters long by one meter diameter, with a cruciform tail, paired elevators and rudders, and an exposed propeller driven by a direct drive low speed DC motor. There are no forward hydroplanes or thrusters and the ballast is preset though there is an emergency abort weight. The hydrodynamic skin is a free flooded fiberglass shell; within are a variety of pressure vessels, allowing maximum flexibility in sensor payload configurations. Power is normally supplied by a disposable battery of up to 5000
alkaline ’D’ cells, though rechargeable cells can be used for shorter missions. Communications and control are based on the LONworks real time networking system which allows an essentially unlimited number of nodes on a four wire bus. Processing power at nodes is augmented by additional processors, e.g., PC or DSP modules, as necessary. Navigation is based on a combination of an optical Inertial Navigation System (INS), a bottom referenced acoustic Doppler sensor, a short baseline acoustic system on the mother ship, a GPS receiver for use on the surface, and various other low performance sensors for use as fallbacks. There is an acoustic communication system capable of low data rates (sub kilobaud) at multi km ranges. Like all current AUV’s, the Autosub design is dominated by the need to conserve energy and to take advantage of scaling laws favoring large vehicles. The inevitable result is a design with limited thrust to weight ratio, poor maneuverability and inability to hover. It is these limitations which make cooperation with other robot types advantageous. More details on Autosub can be found in [2]. The MARUM has a bottom -bound four-wheeled robot C-MOVE [3] which is still in development but already operable (see fig. 2). Most underwater vehicles are operated in a free flying mode which makes thruster propulsion necessary. However, for vehicles that are supposed to investigate the ocean floor bottom traction systems are more favorable. In the ocean mostly caterpillar type propulsion are in use. This results from the fact that corresponding vehicles are for special heavy duty tasks in the offshore business like burying submarine cables, requiring superior drawbar performance. For a completely autonomous operation of an autonomous, underwater measuring vehicle, this concept has to be reconsidered under the aspects of energy efficiency, low impact on the area to be sampled, ease of control and maintenance. The C-MOVE vehicle is a vehicle demonstrator has been developed as part of a cooperation between the University of Bremen/MARUM and the German Aerospace Center (DLR). The partners developed new concepts by combining the expertise in deep sea engineering and planetary exploration technologies. The C-MOVE is designed to operate autonomously down to 6000 m depth in the ocean. It has four wheels for propulsion which are all individually driven by electric motors and can be steered individually. It is meant for investigations in deep abyssal plains to investigate biogeochemical parameters of the water sediment interface. The Robotics Group of the University of Bremen is in the process to develop a new underwater walking robot for rough and steep terrain, which will be based on the bio-mimetic technology of the thoroughly tested walking outdoor-robot SCORPION1 (see fig. 3). The SCORPION robot has eight legs and measures 65cm from front to back. The width depends on the posture of the legs and varies between 20cm and 60cm. In a typical M-shape walking position the robot is 40cm wide. The robot weights 11.5 kg including the 3.0 Ah batteries. 1 sponsored
by DARPA (Grant No. N0014-99-1-0483) and NASA
Fig. 2.
Fig. 3.
The C-MOVE Vehicle
The 8-legged SCORPION Robot
Each leg has 3 DOF: a thoracic joint for protraction and retraction, a basal joint for elevation and depression, and a distal joint for extension and flexion of the leg. Thus the whole system comprises 24 joints. The joints are actuated by 24V, 6W DC-Motors which drive planetary gears. By using its biomimetic control approach the SCORPION is able to walk robustly over a high variety of different substrates like rocks, sand, mud, grass, concrete, and asphalt. Its maximum speed over flat terrain is half of a body length (30cm/sec). It can climb up ramps up to 35% and still overcome small obstacles, like 8cm high pipes. More details can be found in [4], [5]. Possible future fields of application for underwater robots based on legged locomotion are the work in dangerous, highly unstructured, rough, and unpredictable environments, where mobility and the ability to attach itself to structures is critical, e.g., to withstand strong current. We have chosen these platforms because free swimming robots are well suited to map medium scale (10’s to 100’s of meters) structures of the ocean floor, while bottom-bound robots enable small scale measurements (below 1 meter) and allow to explore the physical properties of the sediment
and biological colonization in detail. Furthermore, AUVs like Autosub are not well suited to near bottom operations in complex and steep topography. These robots have different energy needs, different travel range, different communication means, and they comprise very different sensor-systems. Whereas free-swimming AUV perceives the structures at the sea floor remotely by sonar and video, the wheeled and the legged robot can perceive the sea floor directly by interacting with it. Furthermore, the wheeled system can be used to explore large areas of flat ground and the legged system to explore rough terrain. The representation of the environment in the free-swimming AUV and the C-MOVE will include mainly metric data, whereas the representation in the legged robot will be more based on topological data, which will be based on categories the system can learn on the basis of its proprioceptive / tactile data. A complete representation of the environment, which combines these three different sensorviews, would provide a more complete picture and can be used to refine the exploration strategy of each vehicle, e.g., a detailed mapping of a part of the sea-floor structures by the bottom-bound agents would enable the free-swimming robot to lower its security bounds and to move into closer distance to the sea-floor. III. T HE C OOPERATIVE A PPROACH Recently, in the robotics community cooperative approaches to navigation and exploration of unknown environments gained more and more attention, because using groups of robots increased the overall stability of the build maps and reduced the problem of the necessary simultaneous self-localization while mapping (SLAM). Thrun [8] developed an efficient probabilistic algorithm to address this problem in which a team of robots builds a map online, while simultaneously accommodating errors in their odometry. At the core of the algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an online algorithm that can cope with large odometric errors typically found when mapping environments with cycles. The algorithm can be implemented on multiple robot platforms, enabling a team of robots to cooperatively generate a single map of their environment. Reikleitis et. all present in [9] a pair of cooperating robots to test multi-robot environment mapping algorithms based on triangulation of free space. The robots observe one another using a robot tracking sensor based on laser range sensing (LIDAR). The environment mapping itself is accomplished using sonar sensing. The results of this mapping are compared to those obtained using scanning laser range sensing and the scan matching algorithm. They show that with appropriate outlier rejection policies, the sonar-based map obtained using collaborative localization can produce better results than the superior laser range sensing technology. In [10] Burgard et. all consider the problem how the overall exploration time can be efficiently reduced by using cooperative robots. The key problem to be solved in the context of multiple robots is
to choose appropriate target points for the individual robots so that they simultaneously explore different regions of the environment. They present an approach for the coordination of multiple robots which, in contrast to previous approaches, simultaneously takes into account the cost of reaching a target point and its utility. The utility of a target point is given by the size of the unexplored area that a robot can cover with its sensors upon reaching that location. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced for the other robots. This way, a team of multiple robots assigns different target points to the individual robots. This is a good example of a simple coordination of multiple robots, which significantly reduces the exploration time compared to previous approaches. Robots capable of operation at multi km depths are severely constrained by the pressure rating / payload / energy capacity tradeoff. Free swimming AUV’s will probably be unable to afford the weight or energy for six degree of freedom thrusters so will be unable to interact mechanically with the seabed , to hover or to approach close to the seabed in rugged terrain, and their mission duration will be limited to tens of hours by propulsion energy requirements. Bottom robots will have to be relatively small, perhaps 1 - 2 meters across and will have limited capability to move in rugged terrain, to deploy sensors requiring a high viewpoint, to communicate and perhaps to cope with soft sediments. We believe that a cooperating community of different types of robot can overcome many of these difficulties. For simplicity and generality we consider a free swimming AUV a walking robot and a crawler, though other combinations may be advantageous. Possible modes of cooperation include: 1) A preliminary site survey by the AUV operating alone. The AUV is well able to carry out such a survey in a lawn mower pattern at a relatively high and therefore safe altitude using sensors such as sidescan and bathymetric sonars, sub bottom profilers, and cameras. 2) A more detailed planning survey by the robots operating together. Ambiguous but worrying features from (1) would be investigated by directing the crawler to them. 3) An individual robot in difficulty could call on assistance. For example a crawler encountering unexpectedly steep slopes could request a high resolution bathymetric survey by the AUV aimed at finding a safe route around or through the terrain. A survey by a bottom robot might enable a large and not very manoeuvrable AUV to operate closer to the bottom in safety. 4) Navigation in the deep ocean is bound to present difficulties. Use of a short baseline system from the mother ship with errors of tens of meters is of limited value. One solution might be a long baseline acoustic system using bottom mounted transponders; systems of this kind with cm accuracy have been developed for underwater archaeology. Transponders might be placed in suitable positions by the bottom robots who would then com-
mand the AUV to determine the relative positions of the transponder network by acoustic ranging at a high altitude ( tens to hundreds of metres ) where reliable acoustic paths exist. Alternatively , an AUV fitted with an Inertial Navigation System might command the bottom robots to remain stationary without knowing their exact positions and then determine the velocity drift in its INS by acoustic ranging on them. 5) Bottom robots operating in rugged terrain are likely to have communication difficulties. The AUV could alleviate this by acting as a relay station either between the bottom agents or between them and the mother ship. All these forms of cooperation require communication, but the characteristics of the links are unclear, particularly the required data rates and propagation times, whether communication must be strictly deterministic, the extent of data compression and feature extraction necessary for voluminous data such as sidescan images. Authority relationships are also open to question. Should a single robot be in overall charge, responsible for matters such as mission replanning and adaptation to equipment faults ? If so, which ? These are very important questions for which we hope to find answers in future cooperation experiments. For setting up these experiments, it is important, that suitable application scenarios are found, which provide constraints for the vehicles and the possible cooperative strategies. IV. A PPLICATION S CENARIOS The above described differences between the robots will allow investigating the patchiness in an individual physical parameters or the varying biological and chemical conditions within a defined area of interest. A. Hydrothermal Vent Fields These conditions can be found for instance in regions where hydrothermal vents are present. For a future exploration of these regions, it would be of interest to investigate different colonisations with organism depending on the environmental conditions. Typically, in hydrothermal vent regions mussel banks and colonies of tube worms are found in regions close to the orifices of the hydrothermal vents. A specific task for the robot team could be to investigate these regions with hydrothermal vent activity. The free swimming AUV could be used to make a general survey of the region and to identify sources of hot water outflow. After that, the bottom bound robots will be released to spots of interest and will start evaluating the region. The collected information is collected in a central controller and will be updated permanently during the mission. With this information available for all three types of robot, a more focused investigation of the area will be started by redirecting the robots to points of interest or areas where temporal intermittent events occur (geyser). The cooperative approach therefore will allow for a flexible strategy to investigate a region of high variability. To investigate biological phenomena, it is necessary to address the issue of the changing morphology of the seafloor. Some organism can only survive in
a well protected environment which could mean rough terrain. Others like bacteria or musseles are often found in large communities covering more flat terrain. The differing scale coverage of the robots therefore can be used to investigate differing phenomena while integrating common parameters like for instance currents into the overall picture. B. Under Ice Exploration Ice shelves are one of the most inaccessible and most poorly known environments on earth. Knowledge of these regions is fundamental to the understanding of issues such as the role of the ocean in climate change, ice melting, and the biology beneath the ice shelf. Ice shelves are the floating edges of ice sheets that cover Greenland and Antarctica. These ice sheets contain 77 per cent of the world’s freshwater. Ice shelves have sea water cavities beneath them that open to the ocean at their edges. These cavities may be several hundred kilometres long, with water several hundred metres deep under ice up to a kilometre thick. To be able to investigate these huge structures, new concepts have to be developed that necessarily have to utilise autonomous agents. A concerted deployment of heterogeneous, autonomous, mobile robots is attractive to address the above mentioned issues of scientific investigations because they allow for a flexible and targeted observation strategy over long time periods. Beside the topography of the sea floor below the ice, the structure, thickness and the detailed topography of the bottom side of the ice cover, the colonisation with organisms and the feedback between biological and physical processes are scientific objectives of paramount interest. The investigation of long term changes of this sensitive environment and its causes will result in a better understanding of the impact of climate changes on the entire system. Possible application scenarios could be the deployment of the AUV AUTOSUB again to conduct site surveys to find regions of interest and acting as a central relay station for communication purposes. The other two vehicles, C-MOVE and SCORPION, can be deployed directly under the bottom side of the ice shelves to be able to access particular regions that appeared important from the AUV survey. V. C ONCLUSION After outlining and discussing the difficulties and the possibilities of future cooperative underwater robotics, recapitulating, we see a high potential for transferring ideas on cooperative robots into the field of underwater robotics. Especially, because of the high uncertainty of sensor measurements in underwater environments, techniques of cooperative probabilistic mapping approaches are very likely to improve the correctness of self localization algorithms significantly. Furthermore, heterogeneous robots allow to access new areas of high scientific or commercial interest. Thus we believe that it is worthwhile to pursue these ideas and we plan to implement these with the above described robotic systems. As a first step, we will implement a simulation for further evaluation of the foreseen application scenarios.
R EFERENCES [1] S. McPhail Autonomous Underwater Vehicles: Are they the Ideal Sensor Platforms for Ocean Margin Science?. in Ocean Margin Systems , G. Wefer, D. Billett, D. Hebbeln, B.B. Jrgensen, Tj. Van Weering (eds.), pp 79-97, Springer Verlag, Berlin. 2002 [2] P Stevenson, N. W. Millard, S. D. McPhail, J. Riggs, D. White, M. Pebody, J. R. Perrett, A. T. Webb, Engineering an Autonomous Underwater Vehicle for under ice operations. Proc. OMAE 2003. 22nd International Conference on Offshore Mechanics and Arctic Engineering 8-13th June Cancun, Mexico. 2003 [3] C. Waldmann and Eric Epping and the MOVE!-Team, Move! A Deep Ocean Mobile Vehicle for Bio-Geochemical Research. AGU,EGS Conference, Nizza, 2003 [4] D. Spenneberg and F. Kirchner, SCORPION: A Biomimetic Walking Robot. Robotik 2002, v. 1679, pp.677-682, VDI, Germany, 2002 [5] D. Spenneberg, Kevin McCullough and Frank Kirchner Stability of Walking in a Multilegged Robot Suffering Leg Loss. Proc. of ICRA 2004, pp 2159-2164, vol. 3. 2004 [6] 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles, IAV 2004, Lisbon, 5 to 7 July 2004 Autonomous Ocean Sampling Network (AOSN), http://www.mbari.org/aosn/default.htm [7] M. Carreras, J. Batlle, P. Ridao and G.N. Roberts. An overview on behaviour-based methods for AUV control. MCMC2000, 5th IFAC Conference on Manoeuvring and Control of Marine Crafts. Aalborg, Denmark, 23-25 August, 2000 [8] S. Thrun. A Probabilistic Online Mapping Algorithm for Teams of Mobile Robots. International Journal of Robotics Research, v. 20 no. 5, pp. 335363, 2001 [9] I. M. Rekleitis and G. Dudek and E. Milios Experiments in Free-Space Triangulation Using Cooperative Localization. IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1777-1782, Las Vegas, NV, Oct. 27-31, 2003 [10] W. Burgard, M. Moors, F. Schneider Collaborative Exploration of Unknown Environments with Teams of Mobile Robots. In Beetz, M. and Hertzberg, J. and Ghallab, M. and Pollack, M.E. (eds.):Advances in PlanBased Control of Robotic Agents, Springer Verlag, LNCS, 2002.
