Multiple Impedance Control for object manipulation by a dual arm underwater vehicle–manipulator system
Introduction
Exploitation of mobile robotic systems in various types of environment is one of the most interesting topics in recent decades. Although dynamics modeling and control of terrestrial and space robots have been regarded more frequently than underwater robots (Eslamy and Moosavian, 2011, Eslamy and Moosavian, 2010, Alipour and Moosavian, 2011, Moosavian and Papadopoulos, 2007, Moosavian and Rastegari, 2006), the utilization of autonomous underwater vehicles (AUV) in marine technologies has been extended in the last two decades. The first models of such systems were designed and used as small submarines which only were able to move freely in the water and accomplish survey missions such as inspection, subsea observations and oil or gas explorations. As these types of underwater robots were unable to carry out any manipulation tasks and interact with the environment, mounting a dexterous manipulator on the moving base (vehicle) was focused in the early 1990s. This idea resulted in the formation of a new type of underwater robots called underwater vehicle–manipulator systems (UVMS). This type could carry out various tasks such as welding, drilling, object manipulation and any other tasks in which high accuracy and dexterity are necessary. Pioneering models, ODIN, OTTER and VORTEX, were mainly used as research test beds and always worked in water tank conditions. AMADEUS, ANSALDO, SWIMMER, ALIVE and SAUVIM are the next projects in the way of the evolution of UVMSs towards further dexterity and autonomy (Sanz et al., 2010a, Sanz et al., 2010b).
Today, one of the challenging subjects related to the application of autonomous UVMSs in underwater missions is accomplishment of the intervention missions which are performed by manned submersible endowed with robotic arms or by remotely operated vehicles (ROV). The drawbacks of these systems such as reduced time for operation, the human presence in dangerous and hostile environment, very high cost associated with the need for an expensive oceanographic vessel with a heavy crane and cognitive fatigue of the operator are why researchers have recently started efforts to make UVMSs capable of performing complicated intervention missions autonomously. Besides, some recent challenging projects such as removing the oil spill in the Gulf of Mexico (Bleicher, 2011) highlighted the need for faster and more reliable underwater robotic systems. Similarly, some future research plans such as the exploration of extraterrestrial life under the ice-covered oceans of the Jupiter׳s frozen moon (Kumagai, 2007, Kramer, 2013) emphasize the need for highly adaptable UVMSs which can execute complicated missions in unknown environment with high degrees of autonomy.
One of the first efforts to develop Intervention Autonomous Underwater Vehicles (I-AUV) was the OTTER I-AUV by which studies on automatic objects retrieval were started (Wang et al., 1995). Another I-AUV was ALIVE that was developed to be capable of docking to a subsea structure not specifically modified for AUV use (Evans et al., 2003). Sotiropoulos et al. (2012) have presented a two-step method for the determination of the optimum docking pose for an AUV performing an intervention mission. Angeletti et al. (1998) and Casalino (2001) have studied grasping and manipulation of objects, an ordinarily necessary task in interventions, by a robotic work cell composed of two 7 DOF manipulators by mainly focusing on its various aspects of modularity, scalability and configurability. Sun and Cheah (2004) have proposed two simple set point controllers for the coordinated control of multiple cooperative UVMSs holding a common object and presented Lyapanov like functions for convergence and stability analysis of the system. Improvement of early claw-like end effectors by addition of 6-axis force/torque and position sensors to fingers is a different research in which increasing the capability of underwater arms in grasping a wide variety of objects (Meng et al., 2006, Wang et al., 2007). Wang et al. (2008) have noticed the drawbacks of the original impedance control method for controlling the contact force between the fingertips and object and have proposed the position-based neural network impedance control method to resolve them by compensating the uncertainty of finger dynamics, object position and stiffness. Shao et al. (2006) have considered cooperative transportation of a floating object to its destination by three fish-like robots and have studied the implementation of a situation based action selection mechanism to perform this task in the lab environment. Also, Zhang et al. (2007) have proposed a coordination method for cooperative transport task in the particular underwater environment by utilizing the limit cycle approach to control the posture of the robotic fish and adopting the fuzzy logic method to control the transport orientation. CManipulator project (Hildebrandt et al., 2008) is the first deep-see underwater robot capable of detecting previously defined objects autonomously, grasp them and set them down or connect them to other objects. Christensen et al. (2009) have presented a hardware facility to simulate movements of an ROV. The described system is capable to let the ROV react in a realistic way to forces emerging while one of the attached manipulators interacts with an object. Development of a multi-limb manipulation system with tactile force-feedback is the main goal of the SeeGrip project (Lemburg et al., 2011) in which providing the additional modality of haptic feedback for underwater handling of objects in hazy water conditions has been focused. Marani et al. (2008) have described one of the first trials of autonomous intervention performed by SAUVIM. This operation is an underwater recovery mission which contains a sequence of autonomous tasks finalized to search for the target and to securely hook a cable to it in order to bring the target to the surface. Hu et al., 2010, Hu et al., 2011 have studied underwater cooperative box-pushing by three autonomous robotic fish. Lee et al. (2012) have regarded the vision-based object detection and tracking techniques for underwater robots performing manipulation tasks. Design and development of an AUV capable to perceive the environment and autonomously perform simple intervention tasks by means of a specific hand-arm system are long term objectives of the RAUVI project (Sanz et al., 2010b). TRIDENT (Prats et al., 2011) is another recent project in which a new methodology for multipurpose underwater intervention tasks is focused, and a new control architecture, which exploits all redundant degrees of freedom of the AUV to satisfy a set of conditions of scalar inequality type, is proposed (Simetti et al., 2014). Mohan and Kim (2012) have presented an indirect adaptive control based on an extended Kalman filter to make an autonomous UVMS capable to compensate various dynamic and hydrodynamic effects during underwater manipulation tasks. Regarding developments in the structure of underwater manipulators, Fernandez et al. (2013) have adapted a manipulator, which was initially designed to be teleoperated, for the autonomy needs in an AUV by reduction in its dimension and weight, and development in its kinematics model. Also, Yu et al. (2013) have focused the replacement of conventional rigidly connected multi-link arm by a small, deployable, and highly maneuverable agent ROV, which eliminates the need for additional positioning sensors and allows significant size reduction of the agent.
As the key element in underwater intervention performed with autonomous systems is autonomous manipulation, and achievement to such a performance requires the capability of the robotic system in physical contacts with the unstructured environment without continuous human supervision, in this paper, as shown in Fig. 1, the manipulation and installation of a definite object by a dual-arm UVMS has been focused, and the functionality of the Multiple Impedance Control (MIC) (Moosavian and Papadopoulos, 1998), in such a mission has been studied.
Why the MIC has been chosen for such a mission is its efficiency in the presence of flexibility in the robotic system and also in case of impacts with an obstacle (Moosavian and Papadopoulos, 1997). As the MIC requires the mathematical model of the robotic system, initially the UVMS explicit dynamics model including various hydrodynamic effects is developed. Next, the essential assumptions in the implementation of the MIC, i.e. object and end-effectors path planning, grasp condition and object dynamics model are dealt with. Following focus on these prerequisites, the object manipulation on a predefined path and its collision with an obstacle is simulated in 3D space. To be more realistic, actuators saturation is taken into account. After that, in order to compare the obtained results with those of the augmented object model (AOM) (Chang et al., 2000), the same procedure is followed to apply this control scheme for performing the described task. Finally, the effect of model uncertainty, a highly probable disturbance in the underwater environment, on the performance of the MIC is noticed.
Section snippets
Dynamics modeling
In this section, the explicit dynamics model of the considered UVMS which is shown in Fig. 2 is derived. This system is composed of two PUMA type robotic arms mounted on a 6 DOF vehicle. Also, one of the arms is equipped with a remote center compliance (RCC) in order to have the necessary flexibility during the object manipulation and contact with the undersea structure.
MIC implementation
The Multiple Impedance Control (MIC) strategy enforces the same impedance relationship at the end-effectors level and the manipulated object (Moosavian and Papadopoulos, 1998). In case of mobile robots the same as the one considered in this paper, since the cooperating arms are mounted on a moving base (vehicle), the MIC also enforces the same impedance law on the vehicle so that all participating manipulators, vehicle and manipulated object can exhibit the same impedance behavior, as implied
Augmented object model formulation
As Chang et al. (2000) have presented, this method is applicable only to fixed base robotic systems. This is another reason for which the vehicle was supposed to be stationary when its desired path was planned in the previous section.
In this method, the dynamics of the whole system is somehow mapped into the object generalized coordinates. In fact, the dynamics of the manipulated object and the robotic system is expressed by the object state variables as (Zarafshan and Moosavian, 2010)
Simulation results and discussions
In this section, the applications of the MIC and AOM strategies in the manipulation task are simulated, and the performances of these control algorithms are compared. The geometric and mass properties and the hydrodynamic parameters of the UVMS1 and the object are shown in Table 1, Table 2 respectively (Sarkar et al., 2002, Fossen, 1994, McMillan et al., 1995).
Also, the
Effect of model uncertainties
In this section, a highly probable possibility in the underwater environment, uncertainty in the dynamics model, is considered and the efficiency of the MIC is examined in a more practical condition. The uncertainties in the UVMS model are considered as 10% and 20% decrease respectively in the mass and hydrodynamic properties relative to those of the real system. The simulation results are shown in Fig. 22.
As it is seen, except a slower response for the vehicle, there are no other noticeable
Conclusion
In this paper, moving an object to precisely peg in an underwater structure was studied, while impacts due to contact are inevitable. To this end, first an explicit dynamics model of a dual arm UVMS was developed. Next, controller design for the manipulation of a known object by the UVMS arms was elaborated, and the performance of proposed MIC method was compared to that of the previously proposed AOM method. The obtained results showed that the MIC has better tracking errors, and in case of a
References (45)
- et al.
Vision-based object detection and tracking for autonomous navigation of underwater robots
Ocean Eng.
(2012) - et al.
Indirect adaptive control of an autonomous underwater vehicle–manipulator system for underwater manipulation tasks
Ocean Eng.
(2012) - et al.
Multiple-arm space free-flying robots for manipulating objects with force tracking restrictions
J. Robot. Auton. Syst.
(2006) - et al.
Optimal docking pose and tactile hook-localisation strategy for AUV intervention: the DIFIS deployment case
Ocean Eng.
(2012) - et al.
Armless underwater manipulation using a small deployable agent vehicle connected by a smart cable
Ocean Eng.
(2013) - et al.
How to ensure stable motion of suspended wheeled mobile robots
J. Ind. Robot
(2011) - Angeletti, D., Bruzzone, G., Caccia, M., Cannata, G., Casalino, G., Reto, S., Veruggio, G., 1998. AMADEUS: dual arm...
- Bleicher, A., 2011. Gulf Spill One Year Later: Lessons for Robotics....
- Casalino, G., 2001. Dexterous underwater object manipulation via multirobot cooperating systems. In: Proceedings of the...
- Chang, K., Holmberg, R., Khatib, O., 2000. The augmented object model: cooperative manipulation and parallel mechanism...
Introduction to Robotics, Mechanics and Control
Dynamics and cooperative object manipulation control of suspended mobile manipulators
J. Intell. Robot. Syst.
Dynamics modeling of suspended mobile manipulators: an explicit approach with verification
Int. J. Model. Simul.
Grasping for the eabed: developing a new underwater robot arm for shallow-water intervention
IEEE Robot. Autom. Mag.
Guidance and Control of Ocean vehicles
Impedance control: an approach to manipulation
ASME J. Dyn. Syst. Meas. Control
Cooperative box-pushing with multiple autonomous robotic fish in underwater environment
IET Control Theory Appl.
Cited by (43)
Machine learning meets advanced robotic manipulation
2024, Information FusionOn dynamic coupling effects of underwater vehicle-dual-manipulator system
2022, Ocean EngineeringCitation Excerpt :The parameters of the system are presented in Table 2, and the hydrodynamic parameters of the vehicle are shown in Table 3. In the typical underwater intervention scenarios for UVDMS, the two manipulators always perform symmetrical motion to guarantee the stability of the grasping operation (Farivarnejad and Moosavian, 2014), such as the underwater pipe grasping scenario shown in Fig. 4. In Sections 4.1 to 4.5, two manipulators are supposed to move symmetrically.
Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation
2020, Ocean EngineeringCitation Excerpt :In Cui et al. (1999), the Impedance Control (IC) was applied on an Underwater Vehicle-Manipulator System (UVMS) to achieve position/force control at EE. Then, authors in Farivarnejad and Moosavian (2014) proposed a multiple IC scheme for a dual manipulator of an AUV. In Antonelli et al. (2001), Gierlak and Szuster (2017) and Taira et al. (2018), various force controllers were demonstrated for contact interventions of I-AUVs.
A review on underwater autonomous environmental perception and target grasp, the challenge of robotic organism capture
2020, Ocean EngineeringCitation Excerpt :They further applied task priority in the free floating manipulation control by taking higher precision as higher priority criterion, lower priority for camera occlusion, centring distance and height task to guarantee manipulation in sight (Ninad Manerikar et al., 2015). Moreover, coordination control and manipulation method of dual arm underwater vehicle manipulator system (DAUVMS) has been considered in the simulations (Hamed Farivarnejad et al. 2014). In compare with UVMS, DAUVMS is a more redundant system and expected to maintain body stability, realize cooperative manipulation and regulate task priorities for underwater operations (E. Simetti and Casalino, 2015).
Task priority control of underwater intervention systems: Theory and applications
2018, Ocean Engineering