Force-Guided Robotic Assembly Process: Control and Contact-State Recognition

Doctoral thesis (2016)

This thesis addresses developing novel Contact-State (CS) modeling, control strategy, and environment position localization (position searching) for force-guided robotic assembly processes of rigid and ... [more ▼]

This thesis addresses developing novel Contact-State (CS) modeling, control strategy, and environment position localization (position searching) for force-guided robotic assembly processes of rigid and flexible objects. For the CS modeling, the wrench (Cartesian force and torque) signals of the manipulated object are captured for different phases of the considered assembly processes and using the Expectation Maximization-based Gaussian Mixtures Model (EM-GMM), a recognizer is developed for each CS of the assembly. The suggested EM-GMM CS modeling scheme is shown to have excellent Classification Success Rate (CSR) with reduced computational efforts. For the control part, it is shown throughout the thesis that a force-guided robotic assembly process is a hybrid nonlinear system with arbitrary switching signal resulted from the constraints arbitrary switching during the assembly. Furthermore, the robot dynamics is frequently unknown, which is the case in many industrial robots, that would make the force-guided robotic assembly process to be an unknown hybrid nonlinear system with arbitrary switching. In order to overcome such a control challenge, a Decentralized Robust Adaptive Fuzzy Control (DRAFC) strategy is derived that guarantees stable performance under constraints arbitrary switching and unknown dynamics. For the environment position localization, the EM-GMM CS modeling scheme is integrated with a spiral search path and the precise hole position is identified for cases of position uncertainty. Experiments are conducted on a KUKA Lightweight Robot (LWR) doing different force-guided assembly tasks for rigid and flexible objects. Excellent performance is reported for the proposed EM-GMM CS recognition scheme, the DRAFC strategy, and the suggested position searching algorithm. The suggested EM-GMM CS recognition, DRAFC strategy, and position localization schemes are compared with the availably corresponding schemes and the superiority of the suggested schemes is shown. The reasons behind the superiority of the EM-GMM CS recognition scheme are the accommodation of the captured signals non-stationary behavior, employing optimized number of GMM components in the modeling process, and employing the EM algorithm that iteratively increases the log-likelihood. The causes behind the superiority of the DRAFC strategy are addressing the unknown nonlinear dynamics of the robot, accommodating the constraints arbitrary switching, and the robustness against possible dynamics parameters drift. The reasons behind the surpassing of the suggested position localization strategy are the robustness against the surface roughness and reduced computational efforts. The proposed EM-GMM CS modeling scheme, DRAFC strategy, and position searching scheme are applied to the entire peg-in-hole assembly processes of rigid and flexible objects. Excellent Localization Success Rate (LSR) was resulted when using the suggested schemes. Furthermore, the proposed CS modeling scheme, control strategy, and localization approach are applied to a couple of applications in automotive industry; the first one is the camshaft caps assembly of a cylinder head and the other is the air-intake manifold assembly of a powertrain. Efficient force-guided robotic assembly processes are obtained for both considered applications. [less ▲]

Model-Free Robust Adaptive Control for Flexible Rubber Objects Manipulation

in 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015), Luxembourg 8-11 September 2015 (2015, September 08)

This article addresses the control problem of robots with unknown dynamics and manipulating flexible rubber objects of unknown elasticity. The manipulated rubber object is considered to be interacting ... [more ▼]

This article addresses the control problem of robots with unknown dynamics and manipulating flexible rubber objects of unknown elasticity. The manipulated rubber object is considered to be interacting with arbitrarily-switched constraints. Such a kind of robot system is shown to have switched impedance parameters during a task execution that results in an unknown hybrid nonlinear system with arbitrarily switched signal. A Model-Free Robust Adaptive Control (MFRAC) strategy is proposed for such a robot system that is proved to guarantee global stable performance with all closed loop signals are assured to be bounded. The suggested MFRAC strategy relies on the synergy of the Adaptive Fuzzy System (AFS), the Sliding Mode Control (SMC), and the notion of Common Lyapunov Functions (CLF). The AFS relaxes the need for knowing the precise robot dynamics, the SMC adds robustness against the drift of the dynamics parameters, and the CLF accommodates the arbitrary switching of the impedance parameters. The bounds of the impedance parameters are adapted online and incorporated in the MFRAC design such that a convergent performance is achieved. Experiment is conducted on a KUKA Lightweight Robot (LWR) doing flexible rubber peg-in-hole assembly process that falls in the category of systems considered in this article. From the experimental results, excellent tracking performance is reported when using the proposed MFRAC strategy for the considered robotic system despite the dynamics anonymity and the unknown impedance parameters arbitrary switching. [less ▲]

Adaptive sliding mode fuzzy control for unknown robots with arbitrarily-switched constraints

in Mechatronics (2015), 30C

This article addresses the control problem of robots with unknown dynamics and arbitrarily-switched unknown constraints. Such kind of robots will be shown to be unknown hybrid systems with arbitrary ... [more ▼]

This article addresses the control problem of robots with unknown dynamics and arbitrarily-switched unknown constraints. Such kind of robots will be shown to be unknown hybrid systems with arbitrary switching and an Adaptive Sliding Mode Fuzzy Control (ASMFC) strategy is proposed that handles the unknown dynamics of the robot along with the unknown constraints arbitrary switching. The ASMFC is a synergy of finding a Common Lyapunov Function (CLF) between the resulted switched subsystems of the considered robots, employing the Fuzzy Logic Systems (FLS), and the use of the Sliding Mode Control (SMC). The CLF accommodates the constraints arbitrary switching, the SMC adds robustness against possible parameters drift, and the FLS approximates the unknown robot dynamics. All unknown parameters are adapted online and all closed loop signals are guaranteed to be bounded. The proposed strategy is validated by conducting an experiment on a KUKA Lightweight Robot (LWR) doing a typical force-guided peg-in-hole assembly task that falls in the category of robot systems under consideration. Excellent tracking performance is obtained when using the ASMFC strategy. Comparison is conducted with the performance of a PD controller that is widely used in commanding industrial robots and the superiority of the proposed strategy is shown. [less ▲]

Position Identification in Force-Guided Robotic Peg-in-Hole Assembly Tasks

in Procedia CIRP (2014), 22

Position uncertainty is inevitable in many force-guided robotic assembly tasks. Such uncertainty can cause a significant delay, extra energy expenditure, and may even results in detriments to the mated ... [more ▼]

Position uncertainty is inevitable in many force-guided robotic assembly tasks. Such uncertainty can cause a significant delay, extra energy expenditure, and may even results in detriments to the mated parts or the robot itself. This article suggests a strategy for identifying the accurate hole position in force-guided robotic peg-in-hole assembly tasks through employing only the captured wrench (the Cartesian forces and torques) signals of the manipulated. In the framework of using the Contact-State (CS) modeling for such robotic tasks, the identification of the hole position is realized through detecting the CS that corresponds for the phase of the peg-on-hole, that is the phase in which the peg is located precisely on the hole. Expectation Maximization-based Gaussian Mixtures Model (EM-GMM) CS modeling scheme is employed in detecting the CS corresponding for the peg-on-hole phase. Only the wrench signals are used in modeling and detecting the phases of the assembly process. The considered peg-in-hole assembly process starts from free space and as soon as the peg touches the environment with missing the hole, a spiral search path is followed that would survey the whole environment surface. When the CS of the peg-on-hole is detected, the hole position is identified. Experiments are conducted on a KUKA Lightweight Robot (LWR) doing typical peg-in-hole assembly tasks. Multiple hole positions are considered and excellent performance of the proposed identification strategy is shown. [less ▲]

Robust Direct Adaptive Fuzzy Control of Flexible Joints Robots with Time-Varying Stiffness/Damping Parameters

in Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014) (2014, June 02)

In this article, we address the problem of controlling unknown flexible-joint robots with unknown time-varying stiffness and damping parameters. We propose a Robust Direct Adaptive Fuzzy Control (RDAFC ... [more ▼]

In this article, we address the problem of controlling unknown flexible-joint robots with unknown time-varying stiffness and damping parameters. We propose a Robust Direct Adaptive Fuzzy Control (RDAFC) strategy that accommodates the dynamics anonymity and joints stiffness/damping variations. The RDAFC strategy relies on the synergy of the concepts of fuzzy logic approximation and the Sliding Mode Control (SMC). The fuzzy logic approximation relaxes the need for knowing the robot dynamics and the SMC accommodates the parameters variations. We also modify the RDAFC strategy to be suited to the KUKA Lightweight Robot (LWR) and propose a control strategy that can accommodates dynamics anonymity, uncertainty and joints elasticity variations. Experimental results are performed on a KUKA LWR moving in free space with its joints stiffness and damping vary with time in sine and cosine waveforms respectively. From the experiments, we can see that excellent tracking performance is obtained when using the RDAFC strategy despite the joints elasticity parameters time-variance and the robot dynamics unavailability. [less ▲]

Robust Direct Adaptive Fuzzy Control of Switched Constrained Manipulators with Unknown Dynamics

in Joint 45th International Symposium on Robotics (ISR 2014) and 8th German Conference on Robotics (ROBOTIK 2014) (2014, June 02)

In this article, we address the problem of controlling robots with arbitrarily-switched constraints and unknown dynamics. Switching between different constraints of a robot would result in a switched ... [more ▼]

In this article, we address the problem of controlling robots with arbitrarily-switched constraints and unknown dynamics. Switching between different constraints of a robot would result in a switched nonlinear system that does not inherit the behavior of its individual subsystems. In order to guarantee stable performance of robots with arbitrarily switched constraints and unknown dynamics, we propose a Robust Adaptive Fuzzy Control (RAFC) strategy that can guarantee global stable performance under such challenging conditions. The suggested control strategy relies on the synergy of the Sliding Mode Control (SMC) that adds robustness against possible dynamics parameters drift, finding a Common Lyapunov Function (CLF) that guarantees stability under arbitrary constraints switching, and Direct Adaptive Fuzzy System (DAFS) that relaxes the need for knowing the precise robot dynamics. Experiments are performed on a KUKA Lightweight Robot (LWR) doing camshaft caps assembly of an automotive powertrain. The given robotic assembly process falls in the category of switched constrained robots and the efficiency of the suggested RAFC strategy in controlling such a robotic task will be shown. [less ▲]

Contact-state modeling of robotic assembly tasks using Gaussian mixture models

in Procedia CIRP (2014), 23

This article addresses the Contact-State (CS) modeling problem for the force-controlled robotic peg-in-hole assembly tasks. The wrench (Cartesian forces and torques) and pose (Cartesian position and ... [more ▼]

This article addresses the Contact-State (CS) modeling problem for the force-controlled robotic peg-in-hole assembly tasks. The wrench (Cartesian forces and torques) and pose (Cartesian position and orientation) signals, of the manipulated object, are captured for different phases of the robotic assembly task. Those signals are utilized in building a CS model for each phase. Gaussian Mixture Models (GMM) is employed in building the likelihood of each signal and Expectation Maximization (EM) is used in finding the GMM parameters. Experiments are performed on a KUKA Lightweight Robot (LWR) doing camshaft caps assembly of an automotive powertrain. Comparisons are also performed with the available assembly modeling schemes, and the superiority of the EM-GMM scheme is shown with a reduced computational time. [less ▲]

Adaptive Sliding Mode Control of Switched Constrained Robotic Manipulators

in 2013 IEEE International Conference on Industrial Informatics, Bochum 28-30 July 2013 (2013, July)

In this paper, we address the control problem of a constrained robotic manipulators with their constraints continuously switched from one to another. Such a switching in the constraints causes a switching ... [more ▼]

In this paper, we address the control problem of a constrained robotic manipulators with their constraints continuously switched from one to another. Such a switching in the constraints causes a switching function to be inserted in the equation of the robot dynamics which may cause transient instability for the overall system. Two robust control strategies are presented in this paper to handle such switched robotic systems. In the first strategy, we assume that the bounds of the constraints are known. A sliding mode stabilizing controller is developed that can guarantee global stable performance of the given robotic system. In the second one, we relax the assumption of knowing the constraints bounds through deriving update laws for those bounds and new control actions that can guarantee global stable performance under such switching constraints. Simulation is performed for a two link robotic system having two switching constraints. The results obtained from the simulation verify the efficacy of the suggested control strategy. [less ▲]

T-S Fuzzy Contact State Recognition for Compliant Motion Robotic Tasks Using Gravitational Search-Based Clustering Algorithm

in 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013) (2013, July)

In this paper, we address the problem of contact state recognition for compliant motion robotic systems. The wrench (Cartesian forces and torques) and pose (position and orientation) of the manipulated ... [more ▼]

In this paper, we address the problem of contact state recognition for compliant motion robotic systems. The wrench (Cartesian forces and torques) and pose (position and orientation) of the manipulated object in different Contact Formations (CFs) are firstly captured during a certain task execution. Then for each CF, we develop an efficient Takagi- Sugeno (T-S) fuzzy inference system that can model that specific CF using the available input (wrench and pose) - output (the desired model output for each CF) data. The antecedent part parameters are computed using the Gravitational Search- based Fuzzy Clustering Algorithm (GS- FCA) and the consequent parts parameters are tuned by the Least Mean Square (LMS). Excellent mapping and hence recognition capabilities can be expected from the suggested scheme. In order to validate the approach; experimental test stand is built which is composed of a KUKA Light Weight Robot (LWR) manipulating a cube rigid object that interacts with an environment composed of three orthogonal planes. The manipulated object is rigidly attached to the robot arm. The robot is programmed, by a human operator, to move in different CFs and for each CF, the wrench and pose readings are captured via the Fast Research Interface (FRI) available at the KUKA LWR. Using the suggested approach, excellent modeling is obtained for different CFs during the robot task execution. A comparison with the available CF recognition approaches is also performed and the superiority of the suggested scheme is shown. [less ▲]

Robust adaptive control of spacecraft attitude systems with unknown dead zones of unknown bounds

in Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering (2012), 226(7), 947-955

This paper addresses the problem of attitude control of a spacecraft when unknown dead zones of unknown bounds exist at the actuators. A robust adaptive controller with parameter update laws are designed ... [more ▼]

This paper addresses the problem of attitude control of a spacecraft when unknown dead zones of unknown bounds exist at the actuators. A robust adaptive controller with parameter update laws are designed for the spacecraft’s attitude and an asymptotically stable tracking performance is mathematically proven based on the proposed design. Numerical simulation results along with the theoretical proof show that the proposed control scheme can successfully stabilize the attitude of the spacecraft with the unknown actuator dead zones. [less ▲]